WELL/RESERVOIR EVALUATION BY USING PRESSURE TRANSIENT AND MATERIAL BALANCE ANALYSIS OF A GAS WELL IN BANGLADESH. MD.

Transcription

1 WELL/RESERVOIR EVALUATION BY USING PRESSURE TRANSIENT AND MATERIAL BALANCE ANALYSIS OF A GAS WELL IN BANGLADESH. MD. HAFIZUR RAHMAN DEPARTMENT OF PETROLEUM & MINERAL RESOURCES ENGINEERING BUET, DHAKA, BANGLADESH

2 WELL/RESERVOIR EVALUATION BY USING PRESSURE TRANSIENT AND MATERIAL BALANCE ANALYSIS OF A GAS WELL IN BANGLADESH. A Project Submitted to the Department of Petroleum & Mineral Resources Engineering in partial fulfillment of the requirements for the Degree of Master of Engineering (Petroleum) By MD. HAFIZUR RAHMAN Student # DEPARTMENT OF PETROLEUM & MINERAL RESOURCES ENGINEERING - BUET, DHAKA- 1000, BANGLADESH October 2013

3 CANDIDATE S DECLARATION It is hereby declared that this project or any part of it has not been submitted elsewhere for the award of any degree or diploma Signature of the Candidate (Md. Hafizur Rahman)

4 RECOMMENDATION OF THE BOARD OF EXAMINERS The undersigned certify that they have read and recommended to the department of Petroleum and Mineral Resources Engineering, for acceptance, a project entitled WELL/RESERVOIR EVALUATION BY USING PRESSURE TRANSIENT AND MATERIAL BALANCE ANALYSIS OF A GAS WELL IN BANGLADESH. submitted by MD. HAFIZUR RAHMAN in partial fulfillment of the requirements for the degree of MASTER OF ENGINEERING in PETROLEUM ENGINEERING. Chairman (Supervisor) : Dr. Mohammed Mahbubur Rahman Associate Professor, Dept. of Petroleum & Mineral Resources Engineering, BUET Member : Dr. Mohammad Tamim Professor, Dept. of Petroleum & Mineral Resources Engineering, BUET Member : Zaved Choudhury (M.Sc.-Petroleum) Director (Gas) Bangladesh Energy Regulatory Commission (BERC) Karwan Bazar, Dhaka. Date: October 20, 2013

5 Abstract To reach a decision as how best to produce a given reservoir it is essential to know its deliverability, properties, size and initial gas in place (GIIP). Estimating reservoir properties has long been a challenge. Traditionally pressure survey or well testing is conducted to estimate the reservoir properties, which is expensive; also production loss is associated with pressure survey. The equations used for well test analysis are derived from the constant terminal rate solution of the radial diffusivity equation. Well testing data is obtained from a relatively short period of time with a controlled environment. If properly done, the data quality is good and results obtained from this test can be reliable. This technique is used to estimate Skin Factor, Formation Permeability, Reservoir Drainage Area, Average reservoir pressure, distance to faults, Connectivity among well etc. If well testing is done only occasionally, developing a good understanding of the reservoir from well testing alone is often difficult. Conventional Decline Curve Analysis normally used to estimate original gas in place and gas reserves. The development of modern Decline Curve Analysis began in This technique used to analyze and interpret production data and pressure data from wells using Type Curves. This technique also can estimate skin, permeability and gas in place. But most of the time it is impossible to maintain controlled condition to collect undisturbed data of decline curve analysis for a long period of time like short period of time of well testing data. In this study for a well evaluation real cases was analyzed using both well testing and decline analysis. First Skin and Permeability are determined with the help of pseudo pressure versus Horner time. Also non-darcy flow coefficient is determined from deliverability test equation. This Skin, Permeability and non-darcy flow coefficient is a reference point to model a reservoir. Classical material balance and its output GIIP is also another reference point to model a reservoir for modern Decline Curve analysis method The GIIP estimated from classical and flowing material balance methods are 600 BCF and 580 BCF respectively. The same is estimated to be 629 BCF by Fetkovich, 630 BCF by Blassingame and 473 BCF by Arp s method. Except for Arp s the rest of the methods provided reasonably close results. The skin factor estimated from well testing analysis is in good agreement with Decline analysis result. Skin, s from Horner plot is 21.15, from type curve is 20, Fetkovich type curve is 19, and Blassingame is also 19. Rate dependent skin is also detected during well test analysis, which was characterized by non-darcy flow coefficient of [MMscf/D] -1. Skin value is quite high, the major contribution is due to formation damage (s d =13) and the partial completion is not significant (s p =2.15). The permeability estimated from well testing is good agreement with past studies. Permeability, k from Horner plot is md and type curve is 236 md. k was also estimated decline analysis method. Both Fetkovich and Blassingame methods provide k value close to each other but order of magnitude lower than well testing results.

6 Table of contents Page Abstract i Nomenclature vi Acronyms viii Chapter 1 Introduction Objectives Methodology 2 Chapter 2 Well testing Type of well testing Buildup test 4 Radius of investigation Injection test Interference test Type curve Treatment of gas well Gas well pressure behavior Non-Darcy flow and rate dependent skin Gas well deliverability test Flow after flow test Isochronal tests Modified isochronal tests 16 Chapter 3 Material Balance Equation Basic concept of MBE Application of Materials Balance Equation Flowing Material Balance Equation 22 Chapter 4 Analysis of decline and Type curve Decline Curve Analysis 24 Exponential/Harmonic/ Hyperbolic decline Decline Type Curve Analysis Fetkovich type curve Blasingame type curve Agarwal-Garden Rate-Time type curve NPI[Normalized Pressure Integral ] type curve 30 Chapter 5 Result s & Discussion- Well testing 31 Result s & Discussion- Production data analysis 51 Chapter 6 Conclusions 62 Recommendations 63 ii P age

7 Table of contents Page Bibliography 64 Appendix A- [PVT analysis] Recombination of Separator Fluid 66 Apparent Molecular Weight 67 Appendix-B [ The Diffusion Equation and Solutions] Introduction 71 Darcy s Law 74 General Flow Equation 75 Flow Equation assumption for gas well reservoir 77 Solution of the diffusion Equation 79 Transient Flow Equation 79 Semi steady state flow Equation 81 Steady State flow Equation 82 iii P age

8 List of Figures Page 2.1 Schematic profile for drawdown test Buildup test followed by drawdown test profile Typical drawdown test with ETR-MTR-LTR Injection test profile Falloff test plot Schematic profile for Drill stem test Typical type curve matching plot Isothermal variation of Z with pressure plot Pressure and rate history for FAF test FAF test with pseudo-pressure Typical Absolute open flow potential plot Pressure and rate history for isochronal test Pressure and rate history for a modified isochronal test Deliverability plot for an isochronal or a modified Isochronal test (log-log) Typical material Balance plot Material Balance Equation curve with water-influx Flowing P/Z Plot- Constant Rate Production Decline curve-rate/times [exponential, harmonic, and hyperbolic] Typical Fetkovich type curve plot Typical Blasingame type curve plot Pressure trend of bottom hole pressure gauge Pressure gradient with wellbore depth Well schematic diagram W-700A PBU Pseudo pressure vs. Horner Time Curve Pseudo-Pressure Deliverability plot of Well -700A IPR (inflow performance relationship ) Curve Log-Log plot of well testing Semi-log plot with superposition time Pressure and Rate History Plot of test, matching with preferred model curve skin sensitivity on Log-log plot of preferred model Permeability sensitivity on Log-log plot of preferred model IPR (inflow performance relationship ) Curve- Darcy vertical well [IPR Curve after 70% Skin reduction]-darcy vertical well Classical Material Balance plot Flowing Material Balance Flowing Material Balance from commercial software output Arp s plot Fetkovich plot Fetkovich type curve Blasingame type curve plot Production data history match plot 60 iv P age

9 List of Tables Page 3.2 Comparison in between classical and flowing Materials balance Geological data and volumetric original gas in place Pseudo pressure with different flow rate Pseudo-pressure and flow data for FAF Data for flowing pressure with flow rate Production data for classical material of well W-700A Production data for flowing material of well W-700A STGIIP results from different methods Results from different source and compare 61 A-1 Recombination of separator fluid 66 A-2 Apparent Molecular Weight 67 A-3 PVT of reservoir fluid 68 A-4 Gas viscosity calculation with different pressure 68 A-5 PVT properties and pseudopressure 69 v P age

10 Nomenclature Symbol Description Unit A area feet 2 B Darcy coefficient in stabilized gas well inflow equation c isothermal compressibility /psi c t total compressibility /psi C coefficient in gas well back pressure equation C A Dietz shape factor D Non-Darcy flow constant appeared in rate dependent skin e exponential ei Exponential integral function f fraction F production term in material balance equation g acceleration due to gravity feet/sec 2 G gas initially in place bcf G p Cumulative gas production h formation thickness feet k absolute permeability md l length feet m ratio of the initial hydrocarbon pore volume of the gas- cap to that of the oil m slope of the early, linear section of pressure analysis plots m(p)/ψ (p) real gas pseudo pressure psia 2 /cp m (p)/ pseudo pressure for two phases gas-oil flow Ψ (p) n reciprocal of the slope of the gas well back pressure equation n number of moles p pressure psia p c critical pressure P D dimensionless pressure p i initial pressure p pc pseudo critical pressure p pr pseudo reduced pressure p wf bottom hole flowing pressure p ws bottom hole static pressure p pressure drop q production rate MMscf/D q i initial production rate Q gas production rate r radial distance feet r e external boundary radius r D dimensionless radius= r/r w r ed dimensionless radius=r e /r w vi P age

11 Nomenclature r w wellbore radius S mechanical skin S g gas saturation S w water saturation S wc connate water saturation t D dimensionless time t DA dimensionless time,(= t D r 2 w /A) T absolute temperature R T c critical pressure T pc pseudo critical temperature T pr pseudo reduced temperature v volume feet 3 w D dimensionless cumulative water influx w i initial volume of aquifer water w p Cumulative water produced Z Z-factor γ gama, specific gravity (liquid relative to water=1, gas relative gm/cc to air=1 at std condition) difference as positive mu, viscosity cp ρ rho, density Ibf/feet 3 ϕ phi, porosity Ф ef, fluid potential per unit mass Ψ fluid potential per unit volume vii P age

12 Acronyms APGWC BCF ETR FMB GOR GRV HPV LTR MMSCF MMscfd MBE MTR MD NTG PHIE POOH PBU RF RCF RIH SCF SGE Above Probable Gas Water Contact Billion Cubic Feet Early Time Region Flowing Material Balance Gas Oil Ratio Gross Rock Volumes Hydrocarbon Pore Volume Late Time Region Million Standard Cubic Feet Million Standard Cubic Feet per day Material Balance Equation Middle Time Region Measured Depth Net to Gross Effective Porosity Pull Out Of Hole Pressure Buildup test Recovery Factor Reservoir s condition Cubic Feet Run in Hole Standard Cubic Feet Gas Saturation of Effective Porosity viii P age

13 Chapter 1 Introduction The study involves a gas field located in the surma basin of Bangladesh. It started production in 1999 with initial capacity of 60 MMscfd. At that time plant capacity was tested by gradually increasing the flow up to 210 MMSCD. The cumulative production was MMSCF on December 2009 and shut in pressure reaches from 2800 psig to 2300 psig. All wells have been producing gas and condensate since ten years. When a well produces for a long period and yields a lot of production data, the analysis results for this reservoir become more realistic than a new well. Well testing involves analysis of dynamic reservoir behavior in response to changing flow conditions at the well. The dynamic reaction of well bottom-hole pressure to rate changes depends on the reservoir and well properties such as permeability, skin, average reservoir pressure etc. Hence, studying the dynamic pressure behavior in response to appropriately designed sequence of well rate changes provides a way to evaluate some of these properties. This technique has been historically used for evaluation of formation permeability, large-scale reservoir heterogeneities and boundaries, reservoir connectivity, well productivity, and for diagnosing possible well productivity problems. Production material balance calculations are used to relate the average pressure to the cumulative production of the fluid and fluid characteristics, instead of using the transient and pseudo-steady state flow relationship. Well test and the material balance approaches tie up correctly is important for confident reserve determination and for forecasting the performance of a well. It is therefore, important to apply both techniques to the gas field in question. This should provide a good understanding of the well and reservoir conditions and helps to decide if any remedial actions are necessary. It should also indicate reserves and production forecast thus help in the field development decision.

14 2 Introduction 1-1 Objectives The objectives of this study involve acquisition of pressure transient data from the well to help evaluate following information. a) To determine the well deliverability (c,n and AOF) b) Predicting the deliverability potential at various flowing conditions. c) To estimate permeability, skin and average reservoir pressure d) To construct and match IPR (Inflow performances relationship), e) To estimate GIIP of well/reservoir This will help us to predict other reservoir also. 1-2 Methodology: i. Gather flow after flow test data (BHP Survey data) where minimum one buildup followed by minimum three drawdown test. ii. Gather historical production data (wellhead pressure and production rate with time) of production well W-700A. iii. Check where pressure is applicable for pseudopressure and do PVT analysis for pseudo pressure calculation. iv. Conventional analysis of well testing-pbu, Materials Balance and Production decline. v. Check the noise in the data, data quality, look for anomalies and find out the causes. Filter out the anomalies and noises if require. Choose a good decline part for production decline analysis and filter to avoid noise for well testing data. vi. Carryout well testing analysis of W-700A production well by type curve matching determines the reservoir properties (Permeability, Skin factor, Average reservoir pressure etc) by assuming a model and match with pre-calculated results. Commercial software Ecrin (Saphir) is used for this work vii. Gather production history data of well W-700A including initial pressure (Pi), several average reservoir pressure with cumulative production data and do materials balance analysis on excel sheet to find out GIIP. viii. Carryout decline curve analysis of production well W-700A by type curve matching in order to determine the reservoir properties (Permeability, Skin factor etc ) with GIIP. Commercial software Ecrin (Topaze ) is used for this work ix. Compare permeability, skin with well testing result and GIIP with classical and flowing material balance x. To determine the well deliverability (c,n and AOF) and inflow performance relationship curve. xi. If any deviation discussion and recommendation on production well W-700A.

15 Chapter 2 Well Testing Well Testing During a well test, the pressure response of a reservoir to changing production (or injection) conditions is monitored. Since the pressure response depends on the properties of the reservoir, it is possible to estimate some reservoir properties from the pressure response. Well testing has developed both theoretically and practically over the year. Consequently it is extensively covered literature [1-16]. Therefore detailed presentation of the subject is beyond the scope this report. 2.1 Types of well testing The type of test performed is governed by the test objectives. In other cases the choice is governed by practical limitations or expediencies. Various types of well test are defined below Drawdown test In a drawdown test, a well that is static, stable and shut-in is opened to flow. For the purpose of traditional analysis, the flow rate is supposed to be constant, q i =q (Fig 2.1). Rate and pressure are recorded as functions of time. The of drawdown test usually include estimate of permeability, skin factor and, on occasion, reservoir volume. These tests are particularly applicable to (i) new wells, (ii) wells that have been shut-in sufficiently long to allow the pressure to stabilize and (iii) wells in which loss of revenue incurred in a buildup test would be difficult to accept. Draw down test is a good method of reservoir limit testing. Fig-2.1 Schematic profile for drawdown test

16 4 Well Testing Buildup test: In a buildup test, a well which is already flowing (ideally at constant rate) is shut in, and the downhole pressure measured as the pressure buildup (Fig-2.2). Analysis of a buildup test often requires only slight modification of the techniques used to interpret constant rate drawdown test. The practical advantage of a buildup test is that the constant flow rate condition is more easily achieved (since flow rate is zero). Curve with a complicated shape is obtained instead of a straight line in both cases of buildup & drawdown test. Based on radius-of-investigation concept this curve can be divided into three regions. Early-Time Region-during which a pressure transient is moving through the formation nearest wellbore. In this region there is a continuous fluid movement from wellbore to surface or to wellbore from formation (afterflow, a form of wellbore storage) Fig 2.3 Typical drawdown test with ETR-MTR-LTR

17 Well Testing 5 Wellbore storage that cause initial flow from wellbore instead of formation effect is found in this region and can be defined by C. Where, C s = wellbore storage constan, A wb = wellbore area = density of liquid in the wellbore (2.1) Middle-Time Region-during which the pressure transient has moved away from the wellbore and into the bulk formation. In this region radius of investigation has moved beyond the influence of the altered zone near the tested well, and after flow has ceased distorting the pressure test dat. Ideal straight line is observed whose slope is related to formation permeability. This straight line ordinarily will continue until the radius of investigation reaches reservoir boundary. Determination of reservoir permeability and skin factor depends on recognition of the middle-time line. From the diffusivity equation (Appendix A, equation B-46) we get below given equation from transient flow- pr, t p ln 2S And we can write it with help of producing time tp and using superposition theory / (2.2) So here slope, m= and if we plot (p i-p ws ) vs. / we will get straight line in middle time region and from the slope we can determine k permeability. With the value of k we can determine the skin s by following given equation (2.3) Late-Time Region- in which the radius of investigation has reached the well s drainage boundaries. In this late-time region pressure behavior is influenced by boundary configuration, interference from nearby wells, significant reservoir heterogeneities, and fluid/fluid contacts. In this region we can get boundary effect and if the reservoir acts as infinite acting then we will not get late time region and middle time region will continue.

18 6 Well Testing Radius of investigation- The radius-of-investigation concept is of both quantitative and qualitative value in well test design and analysis. By radius of investigation, ri, we mean the distance that a pressure transient has moved into a formation following a rate change in a well. This distance is related to formation rock and fluid properties and time elapsed with rate change and given by the equation, (2.4) The radius-of-investigation concept provides a guide for well test design. This equation also provides a mean of estimating the length of time required to achieve stabilized flow (i,e the required for a pressure transient to reach the boundaries of a tested reservoir). Suppose for a centered cylindrical well of drainage radius re stabilized time ts is found by 948 / (2.5) Injection Test An injection test is conceptually identical to a drawdown test except flow is into the well rather than out of it (Fig-2.4). Injection rates can often be controlled more easily than production rates; however analysis of the test results can be complicated by multiphase effects unless the injection fluid is the same as the original reservoir fluid. Figure 2.4 Injection test profile

19 Well Testing Falloff Test A falloff test measures the pressure decline subsequent to the closure of an injection (Fig-2.5). It is conceptually identical to a buildup test. Figure 2.5 falloff test plot As with injection tests, falloff test interpretation is more difficult if the injected fluid is different from the original reservoir fluid Interference Test In an interference test, one well is produced and pressure is observed in a different well or wells. An interference test monitors pressure changes out in the reservoir, at a distance from the original producing well. Thus an interference test may be useful to characterize reservoir properties over a greater length scale than single-well tests. Pressure changes at a distance from the producer are very much smaller than in the producing well itself, so interference tests require sensitive pressure recorders and may take a long time to carry out. Interference test can be used regardless of the type of pressure change induced at the active well (drawdown, buildup, injection or falloff)

20 8 Well Testing Drill Stem Test (DST) A drill stem test is a test which uses special tool mounted on the end of the drill string. It is a test commonly used to test a newly drilled well, since it can only be carried out while a rig is over the hole. In a DST, the well is opened to flow by a valve at the base of the test tool, and reservoir fluid flows up the drill string (which produces again and shut in again). Drill stem tests can be quite short, since the positive closure of the downhole valve avoids wellbore storage effects. Analysis of the DST requires special techniques, since the flow rate is not constant as the fluid level rises in the drill string. Complications may also arise due to momentum and friction effects, and the fact that the well condition is affected by recent drilling and completion operation may influence the results. Fig-2.6 Schematic profile for Drill Stem Test

21 Well Testing Well test by using Type curve [1, 12, 14] A type curve is a graphical representation of the theoretical solutions to flow equations. Type-curve analysis consists of finding the theoretical type curve that matches the actual response from a test well and the reservoir when subjected to changes in production rates or pressures. The match can be found graphically by physical superposition of a graph of actual test data on a similar graph of type curve(s) and searching for the type curve that provides the best match. Since type curves are plots of theoretical solutions to transient and pseudo-steady-state flow equations, they are usually presented in terms of dimensionless variables, for example, dimensionless pressure, pd dimensionless time, td dimensionless radius, rd, and dimensionless wellbore storage, CD rather than real variable (e.g., p, t, r and C). The reservoir and well parameter, such as permeability and skin, can then be calculated from the dimensionless parameters defining that type curve. Any variable can be made dimensionless when multiplied by a group of constants with opposite dimensions, but the choice of this group will depend on the type of problem to be solved. For example, to create the dimensionless pressure drop, p D, the actual pressure drop p in psi is multiplied by group A with unit of psi -1 or p =Ap; Finding a group A that makes a variable dimensionless is derived from equations that describe reservoir fluid flow. To introduce this concept, recall Darcy equation, which describe the radial incompressible, steady-state flow as expressed by (2.6)..p Where r wa is the apparent (effective) wellbore radius, as defined by equation, r wa =r w e -s Group A can be then defined by rearranging Darcy s equation as: (2.7). Because the left-hand side of the previous equation is dimensionless, the right-hand side must be accordingly dimensionless. This suggests that the term [kh/(141.2 Q B )] is essentially a group A with units of psi -1 that defines the dimensionless variable p D or (2.8). Taking the logarithm of both side of the above equation gives us log. (2.9)

22 10 Well Testing Where Q=flow rate, STB/day, B=formation volume factor, bbls/stb and =viscosity, c p ; For a constant flow rate, equation (2.7) indicates that the logarithm of dimensionless pressure drop, log (P D ), will differ from the logarithm of actual pressure drop, log(p), by a constant amount: log. Similarly, the dimensionless time, t D is given by given equation,. (2.10) Taking the logarithm of both sides of the above equation gives,. log log (2.11) Where t=time, hours, c t =total compressibility, psi -1, = porosity. Hence, a graph of log(p) versus log(t) will have an identical shape (i.e., parallel) to a graph of log(p D ) versus log (t D ), although the curve will be shifted by log[kh/(141.2 Q B )] vertically in pressure and log [ k/( ct )] horizontally in time. This concept is illustrated in Fig 2.6 Fig 2.7 Typical type curve matching plot Not only do these two curves have the same shape, but if they are moved relative to each other until they coincide or match, the vertical and horizontal displacements required to achieve the match are related to these constants in Equations 2.9 and Once these constants are determined from the vertical and horizontal displacements, it is possible to estimate reservoir properties such as permeability and porosity. This process of matching two curves through the vertical and horizontal dis-

23 Well Testing 11 placements and determining the reservoir or well properties is called type-curve matching. Ramey s Type Curves and McKinley s Type Curve are most useful analysis in case of well test. 2.3 Treatment of Gas well [9,14,16] Transient response of gas wells is very different from that of liquid system because the fluid properties vary with changes in pressure. The analytical models presented in earlier chapter for common fluid flow are not directly applicable, and the interpretation of transient test in gas well is more complex. For that this chapter firstly behavior of natural gas, Gas compressibility and viscosity, pseudo pressure will be discussed and modified previous model as per pseudo properties. Then by using field data direct interpretation will be performed Gas well pressure behavior The hypothesis of slightly compressible fluids, used in previous mode to describe fluid flow in a porous medium, is not valid for gas system. In the following sections the behavior of natural gas is presented, and the pressure response of gas wells are compared to those of liquid wells -Gas compressibility and viscosity the compressibility of gas c g is a function of the pressure. For a real gas, the equation of state is defined as: PV=ZnRT, where Z is the real gas deviation factor. For an ideal gas Z=1, and the compressibility is. For a real gas. Z changes with the pressure, and the compressibility is expressed as:. (2.12) In gas systems, the viscosity µ is also a function of pressure. The resulting partial differential equation governing the pressure transient response of real gas is not linear and as opposed to liquid flow, it cannot be solved by analytical method. - Pseudo-pressure is also called real gas potential by Al-Hussainy et all in 1966 introduced Pseudo property to eliminate above mentioned differential equation and made it linearized to very similar to the diffusivity equation for slightly compressible fluids. Pseudo-pressure denoted as m(p) and defined as m(p) 2 (2.13)

24 12 Well Testing Fig-2.8 When pressure is less than 2000 psia, the product µz is almost constant and m(p) simplies into: (2.14) So at low-pressure gas wells, it is thus possible to analyze the test in terms of pressure-squared: p 2 When pressure is higher than 3000 psia, the product µz tends to be proportional to p; then µz can be considered as a constant and the pseudo-pressure m(p) becomes: =(p-p 0 ) (2.15) Another at high pressure wells therefore, the gas behaves like a slightly compressible fluid, and the pressure data can be directly for analysis Gas pressure in between 2000 psi to 3000 psi there is no simplification is available and m(p) must be used for analysis.. log (2.16)

25 Well Testing 13 From this form of equation it can be seen that a plot of m(p ws ) versus log(t p +t)/t gives a straight line of slope m from which flow capacity kh can be calculate by., (2.17) Slope,. (2.18) Rate dependent skin or pseudo-skin, s log 3.23 (2.19) llbore defines the rate of pressure change during the pure 2.4 Non- Darcy flow and rate dependent Skin Due to the high velocity of the flow in the immediate surroundings of the well, inertial effects are frequently not negligible. In some cases, the flow is not even laminar but becomes turbulent. Darcy s law is then no longer applicable in the vicinity of the well, and the inertial and turbulent effect produce an additional pressure drop (Houpeurt, 1959; waternbarger, 1968, Mattar and Brar, 1975). The skin coefficient S, measured during well tests, is expressed with a rate dependent terms as:, (2.20) where D is called the non-darcy flow coefficient. In order to separate the two component of the skin effect, S has to be evaluated at several rates. 2.5 Gas well deliverability test [9,11] A deliverability test is a test to predict the absolute open flow potential (AOFP) of a well, and its deliverability potential under various pipeline backpressures. A stabilized rate is required to be a calculated value, based on the time to pseudo steady state. This calculation corrects the actual extended test rate to a lower estimated stabilized rate. Higher permeability reservoirs will have very little correction to stabilize, where lower permeability reservoirs will have a large correction. In 1936 Rawlins and schellhardt [12] presented an empirical relationship between flow rates and the stabilized flowing pressure Pwf. (2.21) Where the pressure pi and pwf are in absolute units, C and n are two constant terms. The coefficient n can vary from 1 in the case of laminar flow to 0.5 when the flow is fully turbulent. In very low-permeability reservoirs stabilized flow cannot be achieved in a reasonable length of time. In such cases, a satisfactory determination of the stabilized deliverability curve can be based on use of the theoretical equations for pseudopressure stabilized flow like, Ψp Ψp aq bq (2.22)

26 14 Well Testing Where a ln S (2.23) And b50.30 D or b D (2.24) Above equation reflect the degree of turbulence 2 effect in a gas reservoir. It also gives the laminarinertial-turbulent 3 (LIT) flow relationship. So,,. (2.25) Here C is defined as the stabilized performance coefficient, and n is the reciprocal of the slope of the straight line. Extrapolation of this line to the difference between the squares of the average reservoir pressure and the bottom-hole flowing pressure equal to atmospheric pressure defines the AOF. 2.6 Back pressure test (Flow after flow test) The well is produced to stabilized pressure at three or four increasing rate q sc (Figure 2.8) the different flow periods have the same duration. This testing sequence is called a Flow after flow test. In low permeability reservoir, the total production time can be relatively long. Figure 2.9 Pressure and rate history for FAF test When the empirical approach is used, the stabilized pressure p wfi and rate q sc are plotted on log-log scale with (p i 2 -p wf 2 ) versus q sc, as shown in figure If the pseudo-pressure is preferred, the deliverability plot is as shown in figure 2.12

27 Well Testing 15 Figure 2.10 FAF test with pseudo-pressure Figure 2.11 FAF with AOF The intercept a and the slop b of the stabilized deliverability straight line are measured, and the AOF is estimated from equation (2.25). FAF test were first method to evaluate well deliverability. The testing procedure is time consuming, a large volume of gas produced and there is only one buildup test. Since the procedure is not well adapted to transient analysis, the isochronal and modified isochronal test are frequently preferred.

28 16 Well Testing 2.7 Isochronal tests With isochronal tests, the well is again produced at three or four increasing rates but a shut-in period is introduced between each flow. The drawdown periods at q sc,j are stopped during the infinite acting regime after the same production time t p and the intermediate build-ups last until the pressure is back to initial condition p i. The final flow is extended, sometimes with a recorded flow rate, to reach the stabilized flowing pressure. Figure 2.11 is for pressure and rate history and Figure 2.12 deliverability plot of isochronal test. Figure 2.12 Pressure and rate history for isochronal test 2.8 Modified isochronal tests With the modified isochronal sequence, the procedure is similar to isochronal tests except that the Fig 2.13

29 Well Testing 17 intermediate shut-in periods have the same duration as the drawdown period and, as shown in figure below. Only last flow is extended until the stabilized flowing pressure is reached. The total test duration is relatively short, and several build-ups are available for the transient analysis. Figure 2.14 Deliverability plot for an isochronal or a modified Isochronal test, Log-log scale, pressure square method.

30 Chapter 3 Material Balance Material balance is the application of the law of conservation of mass to oil, gas reservoirs and aquifers. It is based on the premise that reservoir space voided by production is immediately and completely filled by the expansion of remaining fluids and rock. As demonstrated later in this chapter, material balance is a useful engineering method for understanding a reservoir's past performance and predicting its future potential. This technique has also been extensively covered as reflected in literature [17-21] To understand and analyze gas reservoirs, the following conditions will be applied. 1. Reservoir hydrocarbon fluids are in phase equilibrium at all times, and equilibrium is achieved instantaneously after any pressure change; 2. The reservoir can be represented by a single, weighted pressure average at any time (Pressure gradients in the reservoir cannot be considered by the method.) 3. Fluid saturations are uniform throughout the reservoir at any time (Saturation gradients cannot be handled.) 4. Conventional PVT relationships for normal gas are applicable and are sufficient to describe fluid phase behavior in the reservoir. 3.1 Basic concepts The general material-balance equation for a pseudo-steady state gas reservoir, neglecting water and formation compressibility s is expressed by: p z p i z i 1 G p G (3.1) It is one of the most often used relationships in gas reservoir engineering. It is usually valid enough to provide excellent estimates of original gas-in-place based on observed production, pressure, and PVT data.

31 Material Balance Equation 19 During the life of a gas reservoir, cumulative production is recorded, and average reservoir pressures are periodically measured. At each measured reservoir pressure the gas z-factor is determined to calculate P/z, and the result plotted as shown in Figure 3.1 below. Notice that Equation 3.1 results in a linear relationship between P/z and Gp. That is, as gas is produced from the reservoir, the ratio P/z should decline linearly for a volumetric reservoir. Note that for an ideal gas, pressure alone would decline linearly. p/z, psia (p/z) i Last measured data point extrapolate (p/z) a G pa =8.5 Bscf G=10 Bscf Cumulative gas produced,bscf Figure 3.1 Typical material Balance plot The relative permeability to water is evaluated at the endpoint; i.e., at residual gas saturation; therefore it is not required to obtain the entire relative permeability curve. The residual gas saturation is assumed constant throughout the entire invaded region. The water flow rate can be estimated using the water influx term, qw = dwe/dt. The resulting modified material balance equation becomes: G p B g G( B g B gi ) G t ( B gt B g ) W e B w W p (3.2) where Gp is the volume of trapped gas in the invaded region of the reservoir and is a function of S gr and average pressure in the invaded region.

32 20 Material Balance Equation The linearized material balance equation for gas reservoirs is: where G represents the original gas in place at standard conditions, and F = total net reservoir voidage F E t 5.615W B G e w (3.3) E t F G p B g W p B w (3.4) E t = E g + E cf = total expansion E g = expansion of gas in reservoir E g B g B gi (3.5) E cf = connate water and formation expansion * B gi E cf B gi S wi c w c f * p p S i (3.6) 1 wi Since G and G p are usually expressed in SCF, the units of B g and B gi are in RCF/SCF. A plot of F/E t vs W e B w /E t should result in a straight line with intercept of G, the original gas in place, and slope related to water influx (see Figure 3.2). W e too small W e correct W e too large F/E t,stb Intercept=G W e B w E t Figure 3.2 Material Balance Equation curve with water-influx

33 Material Balance Equation Application of Material Balance equation: The performance of a normally-pressured gas reservoir depends on gas compressibility effects and water influx. Generally, a straightforward material balance analysis is possible for a normallypressured gas reservoir to estimate the initial gas-in-place. These factors often cause an overestimated value of the initial gas-in-place from a p/z-gp graph of the early production performance. Over the years, several corrections have been proposed to accurately estimate the initial gas-in-place. Some of the application of material balance calculations given here: a) Determine original oil and gas in place in the reservoir; b) Determine original water in place in the aquifer; c) Estimate expected oil and gas recoveries as a function of pressure decline in a closed reservoir producing by depletion drive, or as a function of water influx in a water-drive reservoir; d) Predict future behavior of a reservoir (production rates, pressure decline, and water influx); e) Verify volumetric estimates of original fluids in place; f) Verify future production rates and recoveries predicted by decline-curve analysis; g) Determine which primary producing drive mechanisms are responsible for a reservoir's observed behavior, and quantify the relative importance of each mechanism; h) Evaluate the effectiveness of a water drive; i) Study the interference of fields sharing a common aquifer. A data requirement to accurately apply the material balance method consists of: (i) cumulative fluid production at several times (cumulative oil, gas, and water); (ii) average reservoir pressures at the same times, averaged accurately over the entire reservoir; (iii) fluid PVT data at each reservoir pressure as well as formation compressibility.

34 22 Material Balance Equation 3.3 Flowing Material Balance [20,21,22] The Flowing Material Balance uses the concept of stabilized or "pseudo-steady-state" flow to evaluate total in-place fluid volumes. In a conventional material-balance calculation, reservoir pressure is measured or extrapolated based on stabilized shut-in pressures at the well. In a flowing situation, the average reservoir pressure clearly cannot be measured. However, in a stabilized flow situation, there is very close connectivity between well flowing pressures (which can be measured) and the average reservoir pressure. The pressure drop measured at the wellbore while the well is flowing at a CONSTANT rate is the same as the pressure drop that would be observed anywhere in the reservoir, including the location which represents average reservoir pressure. This is true only if pseudo-steady-state conditions are present. The conventional p/z plot uses the extrapolated straight-line trend of measured shut-in pressures (for gas reservoirs) to predict OGIP. The same can be done with flowing pressures, provided that the pseudo-steady-state assumption is valid. The slope of the resulting line of best fit should be the same as the slope of the conventional p/z line. Thus, the existing line needs only to be "shifted" upwards so that it falls though the initial p/z point. The figure below shows how this is done. (3.7) Figure 3.3 [Flowing P/z Plot- Constant Rate Production] The Flowing Material Balance (FMB) is a new production data analysis method, based on a modified version of the Agarwal-Gardner Rate-Cumulative type curves. The method is similar to a conventional material balance analysis, but requires no shut-in pressure data (except initial reservoir pressure). Instead, it uses the concepts of pressure normalized rate and material balance (pseudo) time to create a simple linear plot, which extrapolates to fluids-in-place.

35 Material Balance Equation 23 Strengths: a. Straight-forward and intuitive method b. Provides analytical fluids-in place estimate without requiring shut-in pressures c. Superior to type-curve methods for estimating fluids-in-place, because data are plotted on a linear scale. Late time data on type-curves tend to be somewhat compressed, due to the logarithmic nature of the plot. Limitations: -Only applies to reservoirs in depletion (similar to p/z plot) -Only applies to reservoirs in pseudo steady state reached Table 3.2 [Comparison in between classical and flowing Material balance] i ii Classical Material Balance p/z plot requires average reservoir pressure Requires lengthy shut in tests to determine average reservoir pressure Flowing Material Balance Flowing bottom hole or wellhead pressure is used Shut-in well is not required as flowing condition pressure data is used. iii iv v Without having initial reservoir pressure and by using intermittent average reservoir pressure this evaluation can be done P/z versus Gp Plot is drown from the average reservoir pressure directly and the GIIP is found from this plot There is an issue of revenue loss due to long shut-in well With flowing bottom-hole or wellhead pressure it is required reservoir initial pressure to complete this evaluation Another plot followed by same slope of flowing pressure data and from initial reservoir pressure need to be drawn to find out GIIP No issue for revenue loss Volumetric method is applied at early stage of a reservoir, with mostly geological and fluid properties data. No production or time dependency is incorporated in volumetric estimates. As production continues, other methods become applicable. Material balance can be applied when about 20% of the initial estimated reserve is produced, or when 10% of initial reservoir pressure has declined. MBE is a powerful tool that helps determine the reserves, recovery factor, and drive mechanism. MBE can be applied to a variety of reservoirs, either with or without water influx.

36 Chapter 4 Production Decline Curve Analysis Analysis of Decline and Type Curve Production-decline analysis is the analysis of past trends of declining production performance, that is, rate versus time and rate versus cumulative production plots, for wells and reservoirs. These methods range from the basic material balance equation to decline- and type-curve analysis techniques. This analysis has also been widely covered as reflected in literature [21-25]. There are two kinds of decline-curve analysis techniques, namely, The classical curve fit of historical production data The type-curve matching technique 4.1 Decline Curve analysis [1, 21, 24] Decline curves are one of the most extensively used forms of data analysis employed in evaluating gas reserves and predicting future production. The decline-curve analysis technique is based on the assumption that past production trends and their controlling factors will continue in the future and, therefore, can be extrapolated and described by a mathematical expression. The method of extrapolating a trend for the purpose of estimating future performance must satisfy the condition that the factors that caused changes in past performance, for example, decline in the flow rate, will operate in the same way in the future. These decline curves are characterized by three factors: Initial production rate or the rate at some particular time Curvature of the decline Rate of decline

37 Decline Curve analysis 25 These factors are a complex function of numerous parameters within the reservoir, wellbore, and surface-handling facilities. Ikoku (1984) presented a comprehensive and rigorous treatment of production decline-curve analysis. He pointed out that the following three conditions must be considered in production-decline-curve analysis: I. The production must have been stable over the period being analyzed; that is, a flowing well must have been produced with constant choke size or constant wellhead pressure and a pumping well must have been pumped off or produced with constant fluid level. II. III. Stable reservoir conditions must also prevail in order to extrapolate decline curves with any degree of reliability. This condition will normally be met as long as the producing mechanism is not altered Production-decline-curve analysis is used in the evaluation of new investments and the audit of previous expenditures. Associated with this is the sizing of equipment and facilities such as pipelines, plants, and treating facilities. Also associated with the economic analysis is the determination of reserves for a well, lease, or field. This is an independent method of reserve estimation, the result of which can be compared to volumetric or material-balance estimates. Arps (1945) proposed that the curvature in the production-rate-versus-time curve can be expressed mathematically by a member of the hyperbolic family of equations. Arps recognized the following three types of rate-decline behavior: (i)exponential decline, (ii) Harmonic decline and (iii) Hyperbolic decline Exponential decline: A straight-line relationship will result when the flow rate versus time is plotted on a semi-log scale and also when the flow rate versus cumulative production is plotted on a Cartesian scale. Harmonic decline: Rate versus cumulative production is a straight line on a semi-log scale; all other types of decline curves have some curvature. There are several shifting techniques that are designed to straighten out the curve that result from plotting flow rate versus time on a log-log scale. Hyperbolic decline: None of the above plotting scales, that is, Cartesian, semi-log, or log-log, will produce a straight-line relationship for a hyperbolic decline. However; if the flow rate is plotted versus time on log-log paper, the resulting curve can be straightened out with shifting techniques. Nearly all conventional decline-curve analysis is based on empirical relationships of production rate versus time, given by Arps (1945) as follows: Where q t = gas flow rate at time t, MMscf/day q i =initial gas flow rate, MMscfd/day t= time, day D i =initial decline rate, day -1 b=arps decline-curve exponent. Based on the type of rate-decline behavior of the hydrocarbon system, the value of b ranges from 0 to 1 and accordingly Arps equation can be expressed by fig (4.1)

38 26 Decline Curve Analysis Figure 4.1 Decline curve-rate/times [exponential, harmonic, and hyperbolic] The area under the decline curve of q versus time between the time t1 and t2 is a measure of the cumulative oil and gas production during this period. Dealing with gas reservoir, the cumulative gas production, G p, G (4.2) Replacing the flow rate, q t, from the equation with three individual expressions that describes besides figure 3.3 and integrating gives the following: Exponential b=0 : Hyperbolic 0<b<1: G (4.3) G 1 (4.4) Harmonic b=1 : G ln (4.5) Where G p(t) =cumulative gas production at time t, MMscf q i = initial gas production rate at time t=0, MMscf/unit time t= time, unit time q t =gas rate at time t, MMscf/unit time D i =nominal (initial) decline rate, 1/unit time

39 Decline Curve analysis Decline type curve analysis Basic concept regarding type carve analysis has already been discussed in previous chapter in 2.4 section. Like type curve analysis of well test, decline type curves analysis have been developed so that actual production data can be matched without special graph paper or the trial-and-error procedure required to match pre-plotted theoretical solutions with actual production data. Type carve analysis allow us to estimate not only original gas in place and gas reserves at some abandonment conditions, but also the flowing characteristics of individual wells. If the diagnostics indicate that boundary dominated flow has not been reached, there is no way to predict ultimate reserve or gas-inplace with any confidence. The most effective type curve methods are described below along with their strengths and limitation Fetkovich [21,22] Fetkovich was the first to extend the concept of using type curves to transient production. The Fetkovich methodology uses the depletion for the analysis of boundary-dominated flow and constant pressure typecurve (originally developed by VanEverdingen and Hurst) for transient production. The most valuable feature of typecurves lies not in the analysis, but in the diagnostics. There are two sets of curves that converge in the centre. Matching data on the left side provides information about the transient behavior of the system (k, s) while the right side gives information about the boundary dominated behavior of the reservoir (OGIP, Area). The left-hand side of the Fetkovich type curves is derived from the analytical solution to the flow of a well in the centre of a finite circular reservoir producing at a constant wellbore flowing pressure. Fetkovich showed that, for all sizes of reservoirs, when transient flow ended, the boundary-dominated flow could be represented by an exponential decline. Description- Depletion analysis is empirical (uses Arps theory) Transient analysis assumes constant flowing pressure Useful for non-volumetric reservoirs with two or more mobile phases Useful for production data where flowing pressure are constant or can be assumed constant. Procedure- Plot (logarithmically) flow rate (q) versus time (t) (alternatively, a normalize rate q/ p) can be plotted if pressures are unknown)

40 28 Decline Curve Analysis Match data on q Dd versus t Dd typecurves Choose depletion (b) stem based on closet curve Strength- Simple to apply and does not require flowing pressure data Does not inherently assume a dominant flow regime (plot does not use superposition time) Empirical nature makes it versatile Limitation- Depletion analysis tends to be non-unique (hyperbolic decline curves are very similar in shape) Only Expected Ultimate Recoverable Reserves (EUR), based on historical operating conditions, can be calculated (not fluid in place) Cannot disassociate the reservoir performance from production constrains Blasingame [21,22] The production decline analysis techniques of Fetkovich is limited in that it does not account for variations in bottom hole flowing pressure in the transient regime, and only account for such variations empirically during boundary dominated flow. In addition, changing PVT properties with reservoir pressure are not considered for gas wells. Blasingame typecurve uses a form of superposition time function that only requires one depletion stem for typecurve matching; the harmonic stem. One important advantage of this method is the typecurve used for matching are identical to those used for Fetkovich decline analysis, without the empirical depletion stems. When the typecurve are plotted using analytical exponential stem of the Fetkovich typecurve becomes harmonic. The significance of this may not be readily evident until considering that, if the inverse of the flowing pressure is plotted against time, pseudo-steady state depletion at a constant flow rate follows a harmonic decline. Type-curve allow depletion at a constant pressure one depletion stem for typecurve matching; the harmonic stem. One important advantage of this method is the typecurve used for matching are identical to those used for Fetkovich decline analysis, without the empirical depletion stems. When the typecurves are plotted using analytical exponential stem of the Fetkovich typecurve becomes harmonic. The significance of this may not be readily evident until considering that, if the inverse of the flowing pressure is plotted against time, pseudo-steady state depletion at a constant flow rate follows a harmonic decline. typecurve allow depletion at a constant pressure to appear as if it were depletion at a constant flow rate. In fact, Blasingame et. al. have shown that boundary-dominated flow with both declining rates and pressures appear as pseudosteady state depletion at a constant rate, provided the rate and pressure decline monotonically.

41 Decline Curve analysis 29 Description- Depletion analysis is analytical Concept of normalized rate, material balance time and pseudo time are used Valid for single-phase volumetric reservoir. Diagnostic plots using these methods are slightly biased towards boundary dominated flow. Procedure- Calculate normalized rate by dividing byp(p p for gas reservoir) Calculate material balance pseudo time Logarithmically plot normalized reate (q/p) versus material balance pseudo time. Choose model (radial, fracture, horizontal) Match q/p versus tc(a) data to q Dd versus t Dd and choose transient r e D stem Strength- Concept of rate-integral allows for a relatively smooth derivative typecurve, which is not normally possible for drawdown data. Limitation- Rate-integral calculations are very sensitive to early-time errors. q l and q id curves can contain large cumulative deviations due to relatively insignificant early-time errors. Rate-integral-derivatives (q id ) does not readily display the different flow regimes but it is useful for pattern recognition Agarwal-Gardner Rate-Time curve [21,22] Procedure- Choose model (radial,fractured,horizontal) Match q/p versus t c (a) data to q D versus t DA (constant rate type curves in welltest format) and choose transient (r e D) stem. Plot the inverse-pressure-derivative (1/DER) Match complimentary type curve (1/DER) to data to fine tune the match. Strength- Inverse-pressure derivative type-curve has similar functionality to pressure transient derivatives (simply the inverse). Thus, different transient flow regimes can be more easily distinguished. The transition from infinite acting to boundary dominated flow occurs at a t DA of o.1, which is a single vertical line, common to all type-curves on the plot. (Such a line cannot be drawn on the Blasingame plot) Limitation- Inverse-pressure derivative is usually too noisy to gain any meaningful interpretation. A suggested improvement to this method is to calculate a pressure integral and base the inversepressure derivative on this. The resulting derivative plot retains most of the characteristics of the raw data derivative, but has much less scatter. Overall, tends to be more non-unique than Blasingame

42 30 Decline Curve Analysis NPI (Normalized Pressure Integral) Type-curves [21, 22, 23] Procedure- Choose model (radial, fractured, horizontal) Match p/q versus tc(a) data to pd versus tda (constant rate typecurves in well-test format) and choose transient (red) stem Plot the pressure-integral (p I ) ; Plot the pressure-integral-derivative (p id ) ; Match complimentary type-curves (pdi) and (PDid) to data to fine tune the match Description- Analysis is the inverse of Agarwal-Gardner Rate-Time Typecurves. Well/reservoir evaluation is very crucial as it is directly related to economical feasibility to produce from a reservoir; whether it is cost worthy or not. In this paper few conventional and type curve methods are discussed though none of them is foolproof. It is thus recommended to use several methods at a time with consideration of their merits and drawback. In some cases final evaluation can be declared average output and some cases several output used as a supporting. As commercial software KAPPA-Topaze can do only Arps (conventional), Fetkovich and Blasingame typecurve so well W-700A will be analyzed by using those methods.

43 Chapter 5 Results and Discussions 5.1 Well testing W-700A well was drilled as exploratory well in upper Bokabil structure, and started producing from BB70 reservoir from 1999 until water breakthrough in As it has been perforated again to produce gas from BB50 of that reservoir it is started to produce at initially rate MMscfd. Now a days production rate is approx. 50 MMscfd/d with FTHP 1600 psig and producing CGR 7 bbl/mmscf and WGR 0.3 bbl/mmscf. Proved reserves estimation of this well uses the assumption that water is very close to the lowest open perforation, and water breakthrough is imminent (based on the experience of BB70 watering out in 2002). This reservoir performance analysis is based on single tank (BB50, 60) Material Balance model matched to the prevailing production & pressure history. Pressure points obtained through BHP (Bottom Hole Pressure) survey would help keep the Material Balance model tuned for optimum production and reserves forecasting, and help understand prevailing drive mechanisms better. PBU was conducted at well W-700A in W-700A is completed with 4-½ tubing (minimum ID in RN nipple) set above a 5 liner. Well W-700A measured depth is 2876 meter, maximum deviation 460@1320 m, sand zone is called BB-50 and perforation depth from 2418 meter to 2515 meter. Reservoir maximum bottom hole pressure and temperature are 3338 psi and 1470 F respectively. Its maximum production rate is 55 MMscfd.

44 32 Results and discussions Gas well-700a pressure transient data Fig-5.1 shows that data collection started 16:58:00 hrs on 17-Mar-2010 and completed 10:30:00 hrs on 19-Mar-2010 by flowing three production rate and one buildup test. Total number data is Fig- 5.1 Bottom-hole gauge (P3973) profile

45 Results and discussions 33 Fig-5.2 shows that data collection gradient data started just after finishing entire test at 10:00 hrs on 19-Mar Initially at 10:30 hrs gauge reading was 2740 psia at 2500 meter depth and 2613 psia at 2000 meter, 2538 psia at 1500 meter, 2367 psia at 500 meter depth. It also shows at surface (wellhead or in lubricator) 2266 psig. This figure also shows temperature gradient with wellhead depth and it starts from F at 2500 meter and end 88 0 F at surface condition. Figure 5.2 Pressure gradient with wellbore depth

46 Well # 700A Christmas Tree : 5-1/8", 5M psi, Vetco Gray Elevation: 5.65m RKB to Tbg Hgr 4-1/2" Tubing, 12.6 ppf, L-80, VamAce 4-1/2" Flow coupling, Vam Ace, 2.95m long 4-1/2" Baker TUSME-5 SCSSSV with Baker 3.812" 'F' Nipple m 4-1/2" Flow coupling, Vam Ace, 2.95m long Current Well Schematic FC BAKER SCSSV FC 20" 94ppf X56 45m Cmt'd w/ 565 sx "G" + 2% CaCl spg D47. Casing stuck off bottom. Hole Size 17-1/2" Mud Wt 8.7 Deviated Well: Max Angle = m Kicked off depth = 610m KB 13-3/8" 68ppf K55 582m Cmt'd to surface w/ 1353 sx "G" + 2.5% gel + 2% CaCl gps D-47 followed by 185 sx "G" + 2% CaCl2. Topped off w/ 400 sx "G" + 2% CaCl2 SHOE TEST = ppge Packer Fluid : 9.6 ppg KCL treated with corrosion inhibitor & O2 Scavenger Est. 1000m MD 12-1/4" 4-1/2" Tubing, 12.6 ppf, L-80 with Special Clearance P110 VamAce couplings MINIMUM ID = 3.456" (RN nipple at m) 4-1/2" Flow coupling, Vam 1.43m long Guiberson Locator Seal Assembly, m PBR Seal Bore of Upper Latch Seal nipple 7" Guiberson Magnum Perm Upper m Millout Extension, 1.61m long 4-1/2" Tubing, 12.6 ppf, VamAce, 11 joints 4-1/2" Flow coupling, Vam 2.95m long Guiberson Locator Seal Assembly, m PBR Seal Bore of Lower m Latch Seal nipple 7" Guiberson Magnum Perm Lower m Millout Extension, 1.61m long FC FC Est. 1318m MD 9-5/8" 47 ppf K m MD / m TVD Cmt'd w/ 345 sx "G" + 2.5% gel +.05 gps D47 followed by 225 sx "G" +.05 gps D gps D gps D604 Open DV 950m sx "G" +2.5% gel + 2% CaCl gps D47 followed by 150 sx "G" + 2% CaCl2 SHOE TEST = ppge 8-1/2" /2" Tubing, 12.6 ppf, VamAce, 1 joints 4-1/2" Flow coupling, Vam 1.43m long Halliburton 'R' m 4-1/2" Flow coupling, Vam 1.43m long 4-1/2" Perforated Joints, 5.43m long 4-1/2" Flow coupling, Vam 1.43m long Halliburton 'RN' Nipple (No-Go m 4-1/2" Flow coupling, Vam 1.43m long 4-1/2" Tubing, 12.6 ppf, VamAce, 1 joints Wireline Re-entry m FC R FC FC RN FC 2055m MD/ 1815m TVD Tested to 16.2 ppge 8.8 7" 29ppf N m MD / m TVD 8.7 BB50 SAND (Boka Bil "B2") Cement Squeezed Perfs: m 2spf 0 deg phasing w/ 2-1/8" EJ TVD Top Perf = 2253m BB 50 BB60 SAND (Boka Bil "D") Cement Squeezed Perfs: m 2spf) 0 deg BB 60 phasing w/ 2-1/8" EJ TVD Top Perf = 2358m BB70 SAND Perfs: m (14.5m) BB 6spf 60 deg phasing w/ 2-3/4", 15 gm Predator charges TVD Top Perf = m BB80 SAND (Boka Bil "E") BB 80 Perfs: m 4spf PBTD : m MD / m TVD TD : 2876m MD / 2626m TVD Cmt'd w/ 475 sx "G" + 2.5% gel gps D81 followed by 375 sx "G" +1.0 gps D gps D gps D gps D47 followed by 125 sx "G" +.15 gps D gps D604 SHOE TEST = ppge New Perforations in BB50 w/ 2 3/8 gun : m m (16.3m),6 SPF m m (16m ), 4 SPF m m (38.2m), 4 SPF HPI 2683m (New PBTD) BJ K-1 Bridge 2775m MD BJ K-1 Bridge 2780m MD 5" 18 ppf P m MD / 2626m TVD Liner set on bottom. Cmt'd w/ 450sx "G" +1.0 gps D gps D gps D gps D47. Plug not bumped. 6.0" 9.5

47 Results and discussions 35 Some geological information regarding this flowing zone of this well and volumetric original gas in place: Table 5.1 Geological data Zone Area GRV Net/Gross PHIE SGE HPV 1/Bg OGIP Acres MM Cubic feet MM Cubic feet BCF BB 50 APGWC ,336, , ,265 So current estimate for BB-50 reservoir OGIP is 1,265 Bcf Where, APGWC GRV HPV PHIE SGE NTG Above Probable Gas Water Contact Gross Rock Volumes Hydrocarbon Pore Volume Effective Porosity Gas Saturation of Effective Porosity Net to Gross Reservoir fluid PVT analysis has been done through recombination of separator fluid, average molecular weight of reservoir gas, gas viscosity with different pressure and finally plotted P wf versus Pseudo pressure of well 700A is represented in Appendix A. With the help of Fig A-1 (Normal pressure to pseudopressure relationship curve) a table for pseudo pressure with different tested flow rate shown next page in Table-5.2.

48 36 Results and discussions Table-5.2 Pseudo pressure with different tested flow rate Pseudo Pressure data in mmpsia 2 /cp Cum time, MMscfd Cum time, MMscfd ~~ ~~~ ~~ ~~ ~~ ~~ ~~ ~~

49 Results and discussions Semi-log or Conventional Analysis As discussed in earlier in chapter Gas Well Testing Buildup Test, W-700A: From the pressure flow data of gauge it is found that, a. First flow rate 30 MMSCFD at 7.33 hrs b. 2nd flow rate 40 MMSCFD at 7.66 hrs c. 3rd flow rate 50 MMSCFD at 7 hrs Producing time or pseudo producing time, tp= 24 X Cumulative_ production, _( mmscf ) most _ recent _ rate, _ mmscfd 30X X X 7 t p = X = 50 = 17.5 hrs Horner time= ( t+t p )/ t t and pseudo pressure data found from gauge and plot the given Horner plot- (5.1) 585 Slope, m=1.3x10 6 psia 2 /cp/cycle hr Ψ(Pws) 1 hr =580 mmpsia 2 /cp 570 Pseudo pressurex Ψ(Pwf0)= mmpsia 2 /cp Horner Time FIG-5.4 [PBU Test: Pseudo Pressure versus Horner Time of Well No-700A] The semi-log plot of pseudo-pressure (Ψ) versus Horner time ( t+t p )/ t is shown in Figure 5.4 from the plot following information are readily obtained-

50 38 Results and discussions Slope, m = 1.3X 10 6 Psia 2 /cp/ cycle. Ψ(P 1hr )= 580X10 6 psia 2 /cp Ψ(P wfo, Δt=o)= 545X10 6 psia 2 /cp Estimating permeability: Ψ(P i )-Ψ(P ws )=57.920X10 6 qsc Psc T kht X10 q Kh= m T sc sc T res sc P sc res ( t t X log ( p ) ) t Equation X Kh= 6 1.3X10 X 520 Kh= X X1.3 K= 318 K= md Estimating Skin: ( P1 hr ) ( Pwfo ) k S =1.151X [ log +3.23] Equation (2.19) 2 m c r HC g 6 ( ) =1.151X [ log +3.23] X x =1.151X [ ] =21.15 From this conventional analysis it is found that permeability is md and true skin Those two parameters will be reference parameter of modeling. Net pay h t =318 feet whereas perforated interval is h p =229 feet. So total skin is the summation of damage formation and contribution from incomplete perforation. So ht h t KH h t S p 1 X ln 2 and S Sd S p hp rw KV hp Where, S p = skin due to incomplete perforation and S d = skin due to damage formation; [12]; S p = 2.15 and S d =13. Therefore the major contributor to the skin factor is formation damage and 10% of total skin is due to incomplete perforation. So partial perforation is not so much contribution to high skin. To reduce the skin reservoir stimulation may be applied. t w

51 Results and discussions Gas deliverability test: [9] As per discussion in earlier chapter two in section 2.6 data has been taken during well testing was followed back pressure test (Flow after flow test) Time required reaching pseudo steady state,. = 0.1 [For circular center exact tda=0.1, ref [18] ].... ; [k= 200 md & drainage area 7411acres from surveyor]. =4.55 hrs, Well testing design 6 hrs for every drawdown test so they are stabilized flow. However in order to use pseudo pressure the given below equation (2.22) will be used Ψp Ψp aq bq and from there we get Ψp Ψp /q abq Table 5.3 Pseudo-pressure and flow data for FAF Shut in m(pr) MM psia 2 /cp Flowing m(p wf ), MM psia 2 /cp Flow rate. Mscf/D [{m(p r )-m(p wf )}/flow rate}; psia 2.D/cp. Mscf Fig 5.5 Pseudo-Pressure Deliverability plot of Well-700A

52 40 Results and discussions Here a=212.4 psi 2 /cp-(mscfd) [ from figure 4.10 ] =212.4X10 3 psi 2 /cp-(mmscfd) and b= psi 2 /cp-(mscfd) 2 = X10 6 psi 2 /cp-(mmscfd) 2 From the equation (2.24) it is found that, b D or X D [Reservoir tem 1430 F and h= feet] So Non Darcy flow coefficient is 0.6 [MMscf/D] -1 or D=0.60 /MMscfd Now rate dependent skin S can be measured by following (2.20) equation, or 30 MMscfd = X 30=48 ; 40 MMscfd = X 40=54 50 MMscfd = X 50=60 And hence deliverability equation - Ψ(p r )-Ψ(p wf )=212.4X10 3 q sc X10 6 q sc 2 (5.2) Table 5.4 Data for Flowing pressure with rate Pseudo Pwf, Psia MMscfd PressureX10 6 Equation: 5.2 From Plot [Fig-A-1] From eq-5.2 Appendix-A

53 Results and discussions 41 Fig 5.6 IPR Curve (Inflow performance relationship) By using equation (5.2) and pseudo pressure converted to normal pressure given FIG-5.6 (IPR Curve) drawn and heree AOFP = 225 MMscfd, from graph. By using equation (2.25) AOFP, = MMscfd, Non-Darcy flow coefficient is 0.6/MMscfd and from the absolute open flow potential AOFP is 225 MMscfd from graph and 255 MMscfd from the equation. These data are also others reference point for software input. In Shapir it is observed that rate dependent skin of Non Darcy flow coefficient matched with (MMscf/D) -1 and this value also used in IPR Curve of Darcy Vertical Well Producer.

54 Log-Log plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Buildup test followed by FAF 1E+8 m(p)-m(p@dt=0) and derivative [psi2/cp] 1E+7 1E+6 1E+5 1E-4 1E dt [hr] xport_with Flow_700A_p load.xlsx [Export_With Flow_700A] build-up #1 Rate 0 MMscf/D Rate change 50 MMscf/D P@dt= psia Pi psia Smoothing 0.1 Selected Model Model Option Standard Model Well Vertical, Variable Skin Reservoir Homogeneous Boundary Circle, No flow Main Model Parameters TMatch 3880 [hr]-1 PMatch 1.75E-6 [psi2/cp]-1 C bbl/psi Total Skin 42.8 k.h, total md.ft k, average 236 md Pi psia Model Parameters Well & Wellbore parameters (W-700A) C bbl/psi Skin0 20 ds/dq [MMscf/D]-1 Reservoir & Boundary parameters Pi psia k.h md.ft k 236 md Re - No flow 5880 ft Derived & Secondary Parameters Delta P (Total Skin) psi Delta P Ratio (Total Skin) Fraction Pbar psia Ecrin v ABT Final_W-700A.ks3 12/3/2013 Page 1/1

55 Semi-Log plot Analysis 1 Company A Gas Field Field Well Tested well Test Name / # Buildupt test followedy FAF 5.5E+8 5.4E+8 m(p) [psi2/cp] 5.3E+8 5.2E Superposition Time xport_with Flow_700A_p load.xlsx [Export_With Flow_700A] build-up #1 Rate 0 MMscf/D Rate change 50 MMscf/D P@dt= psia Pi psia Smoothing 0.1 Selected Model Model Option Standard Model Well Vertical, Variable Skin Reservoir Homogeneous Boundary Circle, No flow Main Model Parameters TMatch 3880 [hr]-1 PMatch 1.75E-6 [psi2/cp]-1 C bbl/psi Total Skin 42.8 k.h, total md.ft k, average 236 md Pi psia Model Parameters Well & Wellbore parameters (Tested well) C bbl/psi Skin0 20 ds/dq [MMscf/D]-1 Reservoir & Boundary parameters Pi psia k.h md.ft k 236 md Re - No flow 5880 ft Derived & Secondary Parameters Delta P (Total Skin) psi Delta P Ratio (Total Skin) Fraction Pbar psia Ecrin v ABT Final_W-700A.ks3 12/3/2013 Page 1/1

56 History plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Buildup test followed by FAF 2730 [psia] [MMscf/D] Pressure [psia], Gas rate [MMscf/D] vs Time [hr] xport_with Flow_700A_p load.xlsx [Export_With Flow_700A] build-up #1 Rate 0 MMscf/D Rate change 50 MMscf/D P@dt= psia Pi psia Smoothing 0.1 Selected Model Model Option Standard Model Well Vertical, Variable Skin Reservoir Homogeneous Boundary Circle, No flow Main Model Parameters TMatch 3880 [hr]-1 PMatch 1.75E-6 [psi2/cp]-1 C bbl/psi Total Skin 42.8 k.h, total md.ft k, average 236 md Pi psia Model Parameters Well & Wellbore parameters (W-700A) C bbl/psi Skin0 20 ds/dq [MMscf/D]-1 Reservoir & Boundary parameters Pi psia k.h md.ft k 236 md Re - No flow 5880 ft Derived & Secondary Parameters Delta P (Total Skin) psi Delta P Ratio (Total Skin) Fraction Pbar psia Ecrin v ABT Final_W-700A.ks3 12/3/2013 Page 1/1

57 Log-Log plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Buildup test followed by FAF 1E+8 m(p)-m(p@dt=0) and derivative [psi2/cp] 1E+7 1E+6 1E+5 1E-4 1E dt [hr] (current) xport_with Flow_700A_p load.xlsx [Export_With Flow_700A] build-up #1 Rate 0 MMscf/D Rate change 50 MMscf/D P@dt= psia Pi psia Smoothing 0.1 Selected Model Model Option Standard Model Well Vertical, Variable Skin Reservoir Homogeneous Boundary Circle, No flow Model Parameters Well & Wellbore parameters (W-700A) C bbl/psi Skin0 20 ds/dq [MMscf/D]-1 Reservoir & Boundary parameters Pi psia k.h md.ft k 236 md Re - No flow 5880 ft Derived & Secondary Parameters Delta P (Total Skin) psi Delta P Ratio (Total Skin) Fraction Pbar psia Ecrin v ABT Final_W-700A.ks3 Main Model Parameters TMatch 3880 [hr]-1 PMatch 1.75E-6 [psi2/cp]-1 C bbl/psi Total Skin 42.8 k.h, total md.ft k, average 236 md Pi psia 12/3/2013 Page 1/1

58 Log-Log plot Analysis 1 Company A Gas Field Field Well Tested well Test Name / # Flow after flow test 1E+8 m(p)-m(p@dt=0) and derivative [psi2/cp] 1E+7 1E+6 1E+5 1E-4 1E dt [hr] md.ft md.ft md.ft md.ft (current) xport_with Flow_700A_p load.xlsx [Export_With Flow_700A] build-up #1 Rate 0 MMscf/D Rate change 50 MMscf/D P@dt= psia Pi psia Smoothing 0.1 Selected Model Model Option Standard Model Well Vertical, Variable Skin Reservoir Homogeneous Boundary Circle, No flow Model Parameters Well & Wellbore parameters (Tested well) C bbl/psi Skin0 20 ds/dq [MMscf/D]-1 Reservoir & Boundary parameters Pi psia k.h md.ft k 236 md Re - No flow 5880 ft Derived & Secondary Parameters Delta P (Total Skin) psi Delta P Ratio (Total Skin) Fraction Pbar psia Ecrin v ABT Final_W-700A.ks3 Main Model Parameters TMatch 3880 [hr]-1 PMatch 1.75E-6 [psi2/cp]-1 C bbl/psi Total Skin 42.8 k.h, total md.ft k, average 236 md Pi psia 12/4/2013 Page 1/1

59 Darcy Vertical Well - Produ... Analysis 1 Company A Gas Field Field Well Tested well Test Name / # Flow after flow test 2000 Pwf [psia] Q [MMscf/D] IPR: Darcy Vertical Well - Producer AOFP MMscf/D Prod. index E-4 [MMscf/D]/psia**2 Pavg 2749 psia Ca Dietz k 236 md h 319 ft rw 4.5 in Skin Total Skin Total skin 20 Drainage area 2500 acre Non Darcy flow coefficient [MMscf/D]-1 Ecrin v ABT Final_W-700A.ks3 12/3/2013 Page 1/1

60 Darcy Vertical Well - Produ... Analysis 1 Company A Gas Field Field Well Tested well Test Name / # Flow after flow test 2000 Pwf [psia] Q [MMscf/D] IPR: Darcy Vertical Well - Producer AOFP MMscf/D Prod. index E-4 [MMscf/D]/psia**2 Pavg 2749 psia Ca Dietz k 236 md h 319 ft rw 4.5 in Skin Total Skin Total skin 6 Drainage area 2500 acre Non Darcy flow coefficient [MMscf/D]-1 Ecrin v ABT Final_W-700A.ks3 12/3/2013 Page 1/1

61 C && n (F) - m(p) - Produce... Analysis 1 Company A Gas Field Field Well Tested well Test Name / # Flow after flow test 1E+9 m(pavg)-m(pwf) [psi2/cp] 1E+8 1E Q [MMscf/D] q BHP Flowing (Pwf) MMscf/D psia IPR: C & n (F) - m(p) - Producer (Flow after flow) AOFP MMscf/D C (trans.) [MMscf/D]/[[psi2/cp]**0.584] n Pavg psia Test points 3 Bottom hole pressures Ecrin v ABT Final_W-700A.ks3 12/10/2013 Page 1/1

62 50 Results and discussions The pressure and rate history of test has been shown in fig-5.9 and there is model simulated line did not match with first 30 MMscfd drawdown test. It is supposed to not matching initial flow with model selected non-darcy flow coefficient. From conventional analysis of Horner plot Fig-5.4 and Log-log plot of fig 5.7 it is found that skin is so high (S=20) and this is for contribution of both partial perforation and formation damage during during drilling/completion. However well's productivity index is high due to high permeability (k=200 md). Non-Darcy flow coefficient is measured with the help of fig-5.5 and its value is 0.6 [MMscfd] -1 but when we started to model it is found that given model is matching with [MMscf/D] -1.except first draw down test. The commercial software package KAPPA Saphir ( ) was used for this analysis. Fig-5.7 to 5.8 shows the pseudo pressure derivative on log-log plot and pseudo pressure on semi-log plot curves along with the respective results. It is seen that permeability, k=236 md skin0, s=20 and non-darcy flow coefficient, D= [MMscf/D] -1. The methodology also involves simulation of pressure versus time with estimated values of k,s, AOF etc as input. The simulated data is matched on real data on fig-5.9. The quality of match indicates the validity of the assumptions of the well/reservoir model. A sensitivity well/reservoir s skin factor varying parameters (s= 6, 10 and 15) comparison with current s=20 is shown in Fig and formation permeability thickness (kh= 31800, and md.ft) comparison with 7372 md.ft shown in fig From the gas deliverability test it is measured that non-darcy flow coefficient is 0.6 [MMscf/D] -1 and AOFP is 225 MMscfd (fig-5.6) from conventional analysis. But model used this coefficient is [MMscfd] -1 and by using Darcy vertical well equation AOFP is found 266 MMscfd. AOFP from conventional analysis of IPR curve Fig-5.6 is less than the value of Darcy vertical well and this is for graphical calculation. On the other hand, the value from the AOFP equation (2.25) it value is 255 MMscfd and this is very close to the value of Darcy vertical well (266 MMscfd). All these discussion regarding non-darcy flow coefficient and absolute open flow potential state that they are very close to each other (model and calculated value) and this also satisfy the model data used for well interpretation. Again from Fig-5.13 IPR curve from Darcy vertical well it observed that 70% reduction of current true skin the open flow potential increased 10% and there is a lot of effect on production index as well as flow efficiency. It also observed that flow rate will be increased 10%-15% of current rate. From stabilized flow gas deliverability equation it is found that when value of n is then AOFP is 266 MMscfd. So in order to match with Darcy vertical well AOFP value of n is and this value indicate that flow is strong turbulence [n=.5 fully turbulence to 1 laminar]. Here also C (trans.) value is [MMscf/D]/[[psi2/cp]**0.584] where where C is the flow coefficient and n is the deliverability exponent and here pseudo pressure is applicable.

63 Results and discussions Production data analysis Classical Materials Balance Table 5.5 production data for classical MBE Date P avg z P avg /z Gp, BCF 1-Sep , Sep , Aug , Mar , Feb , Pavg Vs Gp Feb'12 Linear (Pavg Vs Gp Feb'12) Pavg/Z [psia] Pab@1300 psig 370 BCF Pab@ 300psig BCF GIIP=600 BCF Gp [BCF] Fig-5.15 Classical Material Balance From figure 5.15 it showed GIIP is 600 BCF as per last BHP survey on Feb 2012 and followed depletion drive mechanism as per Fig 3.1. Average reservoir pressure data for table 5.5 are collected from yearly well surveillance program conducted by third party (Schlumberger/Halliburton).

64 52 Results and discussions Flowing Material Balance Date Table 5.6 Production data for Flowing MBE FTHP, Flow, z P FTHP /z Gp, BCF psig MMscfd 10-Sep Apr Mar Feb Jun Pi/zi PFTHP/z v Gp pi/zi v Gp Linear (PFTHP/z v Gp) 2500 p FTHP /z [psia] GIIP=580 Bcf Gp [BCF] Fig-5.16 Flowing Material Balance plot From figure 5.16 it shows that GIIP is 580 BCF with the help of flowing material balance equation. All data collected from field analog gage so there may chance to occur error during data collection from pressure gage.

65 p/z*-q plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Production Data Analysis 4000 Pi/Zi p/z* [psia] STGII Q [bscf] pwf/z pavg/z Ecrin v Hafizur Rahman.ktz 12/8/2013 Page 1/1

66 54 Results and discussions With the help of material balance equation found the following evaluation data. Fig-5.15 GIIP 600 BCF Withdrawal gas up to 31st December BCF; 21% Recovered. Abandonment pressure Expected ultimate recovery (EUR) Remaining gas In BCF RF Recovery Factor NATURAL RECOVERY 1300 PSI 370 BCF 230 BCF 61% By using booster compressor 300 psi 550 BCF 50 BCF 91% After recovering 370 BCF and flow-tubing head pressure will reach 1300 psig it needs to set up a pressure boost up compressor for further production as process operating plant equal to or greater than 1300 psig. Table 5.7 shows the comparison of STGIIP estimated by different methods. Table 5.7: STGIIP in BCF Classical MBE ; (BCF) Flowing MBE; (BCF) Decline type curve- Fetkovich plot ;(BCF) Type curve-blasingame plot; (BCF) Production data Arp s plot; (BCF) Fig shows Arps's exponential plot indicating reserves of approximately 473 bscf. The STGIIP estimated using Material Balance plot (Flowing Materials Balance, Fig-5.17) is approximately 646 BCF.Current production constraint is not adequate to estimate the reserves unless the flowing pressure is relatively constant with time. The main reason is that Arps decline analysis is tied to production constraints; its reserve is calculated by assuming that the flowing pressure is constant with time and the well head pressure data was not converted to downhole pressure. Above comparism table indicates that flowing material balance can reliable substitute classical material unless the reservoir behaves like depletion drive (fig-5.16) with pseudo steady state flow (shown in fig-5.20). It also shows that except Arps plot, rest of the methods resulted in reasonably close results.

67 Results and discussions Decline type curve analysis Production data analysis approaches have advanced significantly over the past few years. There are many different methods published in the literature, but there is no single method that yields the most reliable answer. However, the combination of using all available methods will provide a full picture to the analyst in understanding what is going on, and great level of confidence when all methods agree Topaze is one of those applications that is used in the petroleum industry to manage and analyze production and pressure data. Topaze is a windows-based application for production data interpretation by KAPPA Engineering. Next couple of page Topaze software package output from the production data input is given. The production history plot is shown in Fig that shows gas and reservoir properties and GIIP by using Topaze software, the well head pressure data was converted to downhole pressure at 7467 ft by the "Cullender & Smith" flow correlation. In this well, the traditional decline analyses (Arps plot, Fig. 5.18) will less-estimate the reserves. The main reason is that Arps decline analysis is tied to production constraints; its reserve is calculated by assuming that the flowing pressure is constant with time. Fig shows Arps's exponential plot indicating reserves of approximately 473 BCF. The STGIIP estimated using Material Balance plot (Flowing Materials Balance, Fig-5.17) is approximately 646 BCF. Fig classical materials balance shows GIIP is 600 BCF and decline type curve analysis from Fig 5.21 for Blasingame type curve, Fig-5.20 for Fetkovich type curve shows GIPP 630 BCF. Fetkovich type curve (Fig-5.20) shows some data overlap with the right side and it indicated that flow reached pseudo steady state (Boundary dominate flow). So from classical material balance it is found that reservoir is volumetric (reservoir in depletion) and from Fetkovich plot it is found that flow is pseudo steady state. Thus the application of flowing material balance is valid.

68 Arps plot Analysis 1 Company Natural Gas Plant Field Gas Field Well W-700A Test Name / # Production decline 1: q [Mscf/D], Gas volume [scf] vs t [hr] 2: log(q) [Mscf/D] vs t [hr] E E+10 [scf] : log(q) [Mscf/D] vs log(dt) [hr] 4: log(q) [Mscf/D], Gas volume [scf] vs t [hr] 1E E+10 1E+9 [scf] E+5 1E E+8 Ab. rate (qa) Ab. time (ta) Tmin 0 Day Tmax 3771 Day Arps model b Di 1.91E-4 [Day]-1 qi Mscf/D UR 653 bscf Abandonment Ab. ratio (qa/qi) 1E-3 Fraction Ab. time (ta) E+5 Day Q(ta) 597 bscf Reserve(Tmax) 473 bscf Ecrin v W-700A Pdn data_ar.ktz 10/11/2013 Page 1/1

69 Fetkovich plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Production Data Analysis E E+10 q [MMscf/D], Q [scf] E+9 1E+8 [scf] 0.1 1E E+5 dt [hr] W-700A_BB 50.xlsx [pressure] - W-700A_BB 50.xlsx [rate] Tmin hr Tmax hr Pi psia Selected Model Model Option Standard Model, Material Balance Well Vertical Reservoir Homogeneous Boundary Circle, No flow Main Model Parameters Tmin hr Tmax hr Total Skin 19 k.h, total 3120 md.ft k, average 9.8 md Pi psia STGIIP 650 bscf STGIP 541 bscf Qg(tmax) 108 bscf Model Parameters Well & Wellbore parameters (Reference well) Skin 19 Reservoir & Boundary parameters Pi psia k.h 3120 md.ft k 9.8 md Re - No flow 3890 ft Well Intake Gauge Depth N/A ft Bottomhole Depth ft Derived & Secondary Parameters TMatch 4080 [hr]-1 PMatch [psi2/cp]-1 Abandonment Ab. rate (qa) 0 MMscf/D Ab. time (ta) hr Q(ta) E+10 scf Ecrin v Fetko_Blass_History_plot.ktz 12/3/2013 Page 1/1

70 Fetkovich type curve plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Production Data Analysis 10 1 qdd and QDd E-3 1E-4 1E tdd Re/rwa=10 Re/rwa=20 Re/rwa=50 Re/rwa=100 Re/rwa=200 Re/rwa=1000 Re/rwa=10000 Re/rwa= exp(-tdd) b=0 b=0.1 b=0.2 b=0.3 b=0.4 b=0.5 b=0.6 b=0.7 b=0.8 b=0.9 b=1 Pi psia b Di 3.29E-4 [Day]-1 qi 48.8 MMscf/D UR 418 bscf pwf psia k.h 3390 md.ft k 10.7 md Re 3890 ft rwa 2.1E-9 ft Skin 19 PV 486 MMB STGIIP 629 bscf STGIP 551 bscf W-700A_BB 50.xlsx [pressure] - W-700A_BB 50.xlsx [rate] Tmin hr Tmax hr Ecrin v Fetko_Blass_History_plot.ktz 12/3/2013 Page 1/1

71 Blasingame type curve plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Production Data Analysis qdd, qddi and qddid E-4 1E tdd Re/rwa=4 Re/rwa=12 Re/rwa=28 Re/rwa=80 Re/rwa=160 Re/rwa=800 Re/rwa= Re/rwa=10000 qdd qddi qddid W-700A_BB 50.xlsx [pressure] - W-700A_BB 50.xlsx [rate] Tmin hr Tmax hr Pi psia STGIIP 630 bscf STGIP 552 bscf Re 3900 ft rwa 2.1E-9 ft k.h 2550 md.ft k 8.01 md Skin 19 Ecrin v Fetko_Blass_History_plot.ktz 12/3/2013 Page 1/1

72 Production history plot Analysis 1 Company Natural Gas Processing Field Gas Field Well W-700A Test Name / # Production Data Analysis 40 1E+11 [MMscf/D] 20 5E+10 [scf] [psia] Gas rate [MMscf/D], Gas volume [scf], Pressure [psia] vs Time [hr] Rate q Q q model Q model Pressure Pi p Pbar Main Model Parameters Tmin hr Tmax hr Total Skin 19 k.h, total 3120 md.ft k, average 9.8 md Pi psia STGIIP 650 bscf STGIP 541 bscf Qg(tmax) 108 bscf W-700A_BB 50.xlsx [pressure] - W-700A_BB 50.xlsx [rate] Tmin hr Tmax hr Pi psia Selected Model Model Option Standard Model, Material Balance Well Vertical Reservoir Homogeneous Boundary Circle, No flow Model Parameters Well & Wellbore parameters (Reference well) Skin 19 Reservoir & Boundary parameters Pi psia k.h 3120 md.ft k 9.8 md Re - No flow 3890 ft Well Intake Gauge Depth N/A ft Bottomhole Depth ft Ecrin v Fetko_Blass_History_plot.ktz 12/3/2013 Page 1/1

73 Results and discussions 61 The GIIP estimated from all these methods except Arp s plot range from 580 BCF to 630 BCF. Given the noise in data and quality of match for the type curves, the result may be considered reasonably close. Permeability estimated from well testing (Horner plot and Type curve) are reasonably close, and in line with previous well testing result shown in table 5.8. The modern decline techniques, however, resulted in significantly lower k values (Fetkovich type curve k=10.7 md and Blasingame type curve k=8.01 md). The skin factors, however, showed quite good agreement for all different methods and values are ranging from 21 to 19. As stated earlier the major contribution to the skin came from partial perforation and its formation damage is not that significant.. Table 5.8 Results from different methods. Parameter Current study year 2010 on well W-700A Other studies report on this Horner s plot Type curve Modern decline type curve well, W-700A (conventional (Well testing ) (Production data analysis) Well testing Well testing analysis) Fetkovich plot Blasingame plot Skin (S) Permeabili ty, K (md) Preferred model/res ervoir size N/A Homogeneous, circular-no flow; Re=3900 feet Homogeneo us, Infinitely acting Homogeneous, circular-no flow; Re=5880 feet Homogeneous, circular-no flow; Re=3900 feet Homogeneous, circular-no flow; Re=6150 feet The studies of four years before reservoir was model as infinitely acting and skin was 21 with permeability 278 md. Also after two year later again this well testing was conducted and that time selected model was homogeneous, circular-no flow. Analytical report was skin 21, permeability 173 md and reservoir size Re=6150 feet. The data that we used in this study in 2010 and found that it s skin 20, permeability 236 md and reservoir size Re=5880 feet. So this study of 2010 well testing analysis is fully support to analytical report of year 2012 well testing.

74 Chapter 6 Conclusions and Recommendations 6.1 Conclusions Material Balance (Classical and Flowing), Decline analysis (Classical or Arp s and modern techniques such as Fetkovich and Blasingame) were used to analyze the well testing, production and pressure data of well 700A.The well model was Homogeneous, circular-no flow boundary. The first parameter to compare is GIIP. Except for Arp s plot, rest of the methods showed reasonably close each other and its range are from 580 BCF to 630 BCF. Skin factor S was also close from the two opposite approaches- well testing and decline type curve and its range from 19 to 21. The major contribution to skin is formation damage (72% perforation of total pay thickness contributes only 10% skin of total skin). So skin due to partial perforation has not so severe effect. The permeability k obtained from well testing different significantly from the estimated of decline type curve (permeability value k= md from well testing, k=8-11 md from decline type curve).there is also the presence of non-darcy effect or rate dependent skin. With a non-darcy coefficient [D=0.456 (MMscfd) -1 ] the well testing history showed very good match. Therefore for permeability we would rely more on the well testing results rather than decline type curve. The flow is boundary dominated. To be more precise, it is in pseudo steady state, depletion drive reservoir. The flowing material balance can used, instead of the classical material balance which requires average reservoir pressure p avg. Determination of p avg requires pressure buildup test which involves shut-in the well with revenue loss. So flowing wellhead pressure can be used for estimating GIIP. Well is producing at 70% of AOFP due to plant operating pressure and transportation pipe line pressure high. By keeping plant operating pressure same if production is continued then 61% recovery is possible.

75 Conclusions and Recommendations Recommendations - Skin due to partial perforation has not so severe effect. The major contribution to skin is formation damage. Therefore formation stimulation is recommended instead of re-perforation. There can be another study to find out what type of stimulation will be used depending on nature and lithology of the reservoir. -Investigation may be conducted to see how to incorporate the non-darcy effect in the decline type curves. That may close the gap between permeability k values estimated from well testing and decline type curves. When decline type curves provide close results with well testing then this techniques may be a good alternatives to well testing. -61% recovery for depletion drive gas reservoir is quite low by international standards. But this is due to the constrain of high back pressure in the transmission system. In order to increase recovery, wellhead/inlet booster compressor can be installed or transmission line pressure can be reduced by introducing compressor station on transmission line. Thus the recovery can be increased up to 91%. But this analysis is based on a single well. To achieve such high recovery, more wells/infill wells may be required. A complete reservoir simulation may be conducted to investigate this matter.

76 Bibliography [1] Tarek Ahmed, (2006) Reservoir Engineering Handbook-3 rd Edition, ELSEVIER. [2] WILLIAM D. McCAIN, Jr. (1990), The Properties of PETROLEUM FLUIDS-2 nd Edition, PennWell Books. [3] Tarek Ahmed, (1989), Volume-7, Hydrocarbon Phase Behavior, Gulf Publishing. [4] Dominique Bourdet.,(2002), Handbook of Petroleum Exploration and Production, 3 Series Editor Well Test Analysis: The use of advanced interpretation, ELSEVIER. [5] Tang, Y., Yildiz, T., Ozkan, E., and Kelkar, M Effects of Formation Damage and High- Velocity Flow on the Productivity of Perforated Horizontal Wells. SPE Res Eval & Eng8 (4): SPE PA [6] McAleese, Stuart.; (2002), Operational Aspects of Oil and Gas Well Testing, ELSEVIER. [7] Lyons, William C.; Plisga, Gary J.; (2002), Standard Handbook of Petroleum and Natural Gas Engineering (2 nd Edition), ELSEVIER [8] Charidimos E. Spyrou SPE, Schlumberger, Peyman R. Nurafza, SPE, E.ON E&P, Alain C. Gringarten, SPE, Imperial College.; (2002). Well-head Pressure Transient Analysis, SPE MS. [9] JOHN CUBITT -Handbook of Petroleum Exploration and Production- 3, Series Editor [10] M.M Levitan, SP, and M.J. Ward. SPE BP plc,. J.-L. Boutaud de la Combe, SPE, Total S.A; and M.R. Wilson- The use of Well Testing for Evaluation of Connected Reservoir Volume, SPE [11] Johnston, J.L., Lee, W.J., Blasingame, T.A., Texas A and M U.; SPE 1991, Estimating the Stabilized Deliverability of a Gas Well Using the Rawlins and Schellhardt Method: An Analytical ApproachWell,SPE [12] W. John Lee, (1981). SPE Text Book Series Vol-1, Well Testing [13] Ibrahim Sami Nashawi, SPE, Fuad H. Qasem, SPE, Ridha Gharbi, SPE, and Mohammad I. Mir, Kuwait University Gas Well Decline Analysis Under Constant-Pressure Conditions, Wellbore Storage,Damage, and Non- Darcy Flow Effect,SPE [14] Chaudhry, Amant U., (2003), Gas Well Testing Handbook, ELSEVIER. [15] Dake, L. P. (1978), Fundamentals of Reservoir Engineering, ELSEVIER. [16] Firki J. Kuchhuk, SPE, Radius of Investigation for Reserve Estimation from Pressure Transient Well Test, Schlumberger -SPE

77 Bibliography 65 [17] Amit Madahar and Stewart George, Weatherford International Ltd., and A.C. Gringarten, SPE, Imperial College London, Effect of Material Balance on Well Test Analysis, SPE [18] Donnez, Pierre; (2007), Essential of Reservoir Engineering, Editions Technip. [19] Ahmed, Tarek; Meehan, D. Nathan.; (2012), Advanced Reservoir Management & Engineering-2 nd Edition, Chapter 4.2 The Material Balance Equation, ELSEVIER. [20] L. Mattar, D.M.Anderson, (2003), A Systematic and Comprehensive Methodology for Advanced Analysis of Production Data, SPE [21] L. Mattar, R. McNeil, (1998), The Flowing Gas Material Balance, JCPT, Volume 3 7# 2 [[22] Satter, Abdus; Iqbal, Ghulam M.; Buchwalter, James L.; (2008). Practical Enhanced Reservoir Engineering- Assisted with Simulated software, Chapter 12-Material Balance Method and application, PennWell. [23] B.D Poe Jr. W.K Atwood, and K. Brook, SPE, Characterization of Reservoir Properties Using Production Log, Schlumberger- SPE [24] Fanchi, John R (2006), Principles of Applied Reservoir Simulation-3 rd Edition, chapter 2.4 Decline curve Analysis, ELSEVIER. [25] M.J. Fetkovich.; (1980), Decline Curve Analysis using Type curve, SPE-4629-PA.

78 Appendix A Reservoir fluid PVT analysis Test Separator gas and liquid are sampled and with analysis report form laboratory, production gasoil ratio and recombination 1 of surface fluid to well stream. Composition Component Separator Gas,mole fraction, Ib mole i SP Table A-1 Recombination of separator fluid Ib mole I SP gas/ib mole SP Well No-700A Separator Liq,mole fraction, Ib mole i SP Ib mole in well stream/ib mole SP Recombined gas, Ib mole i in well stream/ Ib mole well stream gas/ib mole SP gas, Yi Liq, 49*Yi liq/ib mole SP liq, Xi Liq, Xi+49*Yi N CO H2S CH C2H C3H i-c4h n-c4h i-c5h n-c5h pseudo C6H pseudo C7H pseudo C8H pseudo C9H pseudo C10H pseudo C11H C Molar ratio Ib mole SP gas/ Ib mole SP liq= 0.98/.0200=49 from GOR value 2 Recombination formula from reference [2] chapter 7

79 Appendix A 67 Table A-2 Apparent Molecular Weight Apparent molecular Weight, MWa Reservoir gas, Component Yi MWi Yi*MWi N CO H2S CH C2H C3H i-c4h n-c4h i-c5h n-c5h pseudo C6H pseudo C7H pseudo C8H pseudo C9H pseudo C10H pseudo C11H C MWa Gas Specific Gravity

80 68 PVT Analysis of Well W 700A Table A-3 PVT of reservoir fluid Well No-700A Reservoir Fluid PVT Component Composition(yi) Tci (or) YiTci Pci YiPCi N CO H2S CH C2H C3H i-c4h n-c4h i-c5h n-c5h pseudo C6H C C7+ Molecular weight Ib/Ib mole and Specific gravity From Fig 3-20 Page 116, Petroleum fluid- McCain 2nd Ed, we get- Tc=1100 R Pc= 430 Psia Tpc= R Ppc= psia Table-A-4 Gas viscosity calculation with different pressure Tpc R Tres F Pres= 2745 psia Ppc psia Tres R Ppr Tpr µ g /µ 1 (Fig 3-11) µ g GasS Grv µ µ g /µ µ gi

81 Appendix A 69 Table A-5 PVT properties and pseudopressure Calculation of PVT Properties and Pseudopressure Pwf (psia) Z (Fig-3.5) µ (cp) 2 (P/µZ) Mean 2(P/µZ) ΔP (psia) Mean2(P/µZ)X P Ψ(Pwf), mmpsia 2 /cp Table A-6 Normal Pressure versus Pseudo pressure Pwf Ψ(Pwf), mmpsia 2 /cp

82 70 PVT Analysis of Well W 700A Fig A-1 Pwf Vs. Pseudo Pressure of Well No-700A 1200 Pseudo Pressure mmpsia2/cp Pwf, psia Fig A.1 Normal pressure to pseudopressure relationship curve

83 Appendix B The Diffusion Equation and Solutions Introduction Based on the three laws introduced in Chapter 2 the diffusion equation, equation (2.2 and 2.3), can be derived. In the next section of this appendix, this derivation is given in more detail. Based on equation (2.2), and the conditions described in section (2.1-2) solutions of the diffusion equation can be derived. The details of the derivations are given in the third and last section. Basis of Pressure Analysis All pressure analysis techniques are derived from solutions to the partial differential equations that describe the flow of fluids through porous media, utilizing various boundary conditions. This mathematical description of fluid flow is based on three physical principles: (1) the law of conservation of mass, (2) Darcy's law, and (3) equations of state. Law of Conservation of Mass The law of conservation of mass is referred to as the equation of continuity, and states that for any given system, rate of mass accumulation = (rate of mass in) - (rate of mass out) To state this mathematically, consider the thin cylindrical shell of Figure 1

84 72 Diffusion Equation and Solutions Figure 1 This cylinder has a radius r, thickness r, and a height h. The mass of the fluid in this shell is the fluid density multiplied by the shell volume and the porosity, or mass = If the fluid is flowing, the change of mass in the shell for a time Dt is (B-1) By the law of conservation of mass, this mass change must equal the mass flowing into the shell at r, minus the mass flowing out of the shell at r - Dr. The mass flowing into the shell in time t is the surface area at r multiplied by the fluid density and radial velocity. The mass flowing out of the shell in time t is the surface area at r - Dr multiplied by the fluid density and radial velocity, or mass in = mass out = (B-2) (B-3) By the law of conservation of mass equations, B-1, B-2, and B-3 are combined to give (B-4)

85 Appendix B 73 Dividing equation B-4 by ( ) yields (B-5) Since = r - Dr, the last term in Equation B-5 is Taking the limits as Dr > 0 and t > 0, Equation B-5 reduces to a partial differential equation: Equation B-6 is the continuity equation. (B-6)

86 74 Diffusion Equation and Solutions Darcy's Law Darcy's law states that the volumetric rate of flow per unit surface area at any point in a porous medium is proportional to the potential gradient in the direction of flow at that point (Figure 2). By Darcy's law, the rate of flow at r is given by Figure 2 u = (B-7) where f is the gradient of the potential, f, given by For radial flow: + constant (B-8) (B-9) and neglecting gravity: Therefore, Darcy's law for radial flow can be expressed mathematically as (B-10) (B-11)