Evaluation and Accurate Estimation from Petrophysical Parameters of a Reservoir

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1 American Journal of Environmental Engineering and Science 2016; 3(2): ttp:// ISSN: (Print); ISSN: (Online) Evaluation and Accurate Estimation from Petropysical Parameters of a Reservoir Alabi O. O. 1, Sedara S. O. 2, * Keywords Reservoir, Porosity, Hydrocarbon Saturation, Relative Factor, Hydrocarbon Volume Received: November 10, 2015 Accepted: February 19, 2016 Publised: Marc 24, Department of Matematical and Pysical Sciences, Osun State University, Osogbo, Osun State, Nigeria 2 Department of Pysics and Electronics, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria address geosciencealabi@yaoo.com (Alabi O. O.), bigsamzone@yaoo.com (Sedara S. O.) * Corresponding autor Citation Alabi O. O., Sedara S. O. Evaluation and Accurate Estimation from Petropysical Parameters of a Reservoir. American Journal of Environmental Engineering and Science. Vol. 3, No. 2, 2016, pp Abstract Te accuracy of te resource estimation of a reservoir depends on te correct computation of te reservoir properties from a well log data. Te results for water saturation S w, and effective porosity Ν eff, can be combined to predict ydrocarbon volume. Hydrocarbon volume estimates are obtained by integrating te ydrocarbon pore volume over te volume of interest. Inaccurate estimation of ydrocarbon water saturation vis-à-vis ydrocarbon saturation could lead to predictions deviation in field resources appraisal. Te main aim of te paper is to examine te possible error in evaluating ydrocarbon in place by volumetric metod and its magnitude. Te accepted formula for computation of Petropysical parameters is tagged te Correct Estimation () formula wile te unaccepted formula is tagged Incorrect Estimation (I) formula. Tese formulae were used to compute te average porosity, water saturation and ydrocarbon saturation. Te I formula always gives iger estimation tan te formula. Te ratio of te result obtained by I to is refers as Relative Factor (RF). Te Relative Factor (RF) is te measure of te magnitude of error in estimation for eac parameter. It is concluded tat tat te relative factor (RF) wic is te measure of te magnitude of te possible error increases wit increasing number of zones in a reservoir well and tese errors are more associated wit te computation of ydrocarbon saturation and could ave a significant cost effect in te estimation of resources. 1. Introduction A reservoir is a subsurface rock tat as effective porosity and permeability wic usually contains commercially exploitable quantity of ydrocarbon. Reservoir caracterization is undertaken to determine its capability to bot store and transmit fluid. Hence, caracterization deals wit te determination of reservoir properties/parameters suc as porosity (Φ), permeability (K), fluid saturation, and Net Pay tickness. Permeability is one of te fundamental properties of every oil reservoir rock. Te oil in a reservoir can be extracted troug core only if te rock is permeable, tat is to say if te pores are interconnected. Permeability is te capacity of a reservoir rock to permit fluid flow. It is a function of interconnectivity of te porevolume; terefore, a rock is permeable if it as an effective porosity. Te fluid saturation is te proportion of tepore space tat is occupied by te particular fluid. A reservoir can eiter be water saturated

2 American Journal of Environmental Engineering and Science 2016; 3(2): (S w ) or ydrocarbonsaturated (1-S w ) depending on te type of fluid it contains. Saturation is a relative measurement and commonly expressed in decimal/fractional units or else as a percentage. A good reservoir is one tat is commercially productive; it produces enoug oil or gas to pay back its investors for te cost of drilling and leaves a profit. Porosity wic is a measure of reservoir storage capacity is defined as te proportion of te total rock volume tat is void and filled wit fluids. Porosity is a relative measurement and commonly expressed in decimal/fractional units or else as a percentage [2, 3, 4]. It is worty to note tat te understanding of te depositional setting of a field is fundamentally important in te determination of reserves and in te design of optimum reservoir management procedures. Sands deposited in different depositional environments are caracterized by different sand body trend, sape, size, and eterogeneity. Tis tends to sow tat te pysical caracteristics of reservoir rocks reflect te response of a complex interplay of processes operating in depositional environments. Hence, te reconstruction of depositional environments in successions provides optimum framework for describing and predicting reservoir quality distribution. Also, knowledge of depositional environment of reservoirs troug accurate description/interpretation of wire line logs and core data allows for a better understanding of reservoir caracteristics and ence its quality foroptimal utilization of te embedded resources [2, 3, 9, 11]. ater saturation (S w ) determination is te most callenging of petropysical calculations and is used to quantify its more important complement, te ydrocarbon saturation (1 S w ). Complexities arise because tere are a number of independent approaces tat can be used to calculate S w. Te complication is tat often, if not typically, tese different approaces lead to somewat different S w values tat may equate to considerable differences in te original oil in place (OOIP) or original gas in place (OGIP) volumes. Te callenge to te tecnical team is to resolve and to understand te differences among te S w values obtained using te different procedures, and to arrive at te best calculation of S w and its distribution trougout te reservoir vertically and aerially. In OOIP and OGIP calculations, it is important to remember te relative importance of porosity and S w. A 10% pore volume (PV) cange in S w as te same impact as a 2% bulk volume (BV) cange in porosity (in a 20% BV porosity reservoir). ell log is one of te most fundamental metods for reservoir caracterization, in oil and gas industry, it is an essential metod for geoscientist to acquire more knowledge about te condition below te surface by using pysical properties of rocks [2, 7, 9]. Tis metod is very useful to detect ydrocarbon bearing zone, calculate te ydrocarbon volume, and many oters. Some approaces are needed to caracterize reservoir, by using well log data, te user may be able to calculate: sale volume (V s ), water saturation (S w ), porosity (φ), permeability (k), elasticity (σ, AI, SI, etc.), reflectivity coefficient (R) and oter data tat te user needs. Te interpretation of well log data must be done in several steps and it is not recommended for te user to analyze tem randomly because, te result migt be a total error. Basically, tere are two types of properties tat will be used in reservoir caracterization; tey are petropysics (sale volume, water saturation, permeability, etc.) wic are more geology-like and rock pysics (elasticity, wave velocity, etc.) wic are more geopysics-like. Tere are many tecniques to find a ydrocarbon bearing zone, suc as te RHOB-NPHI cross over (wit some corrections), reflectivity coefficient (just like in seismic interpretation), AI anomaly, etc. Every metod as its weaknesses, so it is best to use every metod to acquire te rigt result [7, 11]. 2. Teoretical Background Te migration of small solid materials ( fines ) witin porous media as long been recognized as a source of potentially severe permeability impairment in reservoirs. Tis impairment as a strong effect on te flow capability (relative permeability) of te reservoir rock. Fines migration occurs wen loosely attaced particles are mobilized by fluid drag forces caused by te motion of fluid witin te pore space. One of te primary factors tat determine te migration of clay particles is te brine composition. Laboratory studies ave sown tat brine salinity, composition and ph can ave a large effect on te microscopic displacement efficiency of oil recovery by water flooding and imbibitions. Several experiments ave sown tat injection of brine can improve oil recovery from nature core (or reservoir). Tis is possible because increase in salinity of water in core pores increases permeability of te core and tis, increase oil recovery. Data from experiment on Berea cores by [3, 5, 8] sows tat oil recovery via imbibitions increase significantly wit increasing salinity of connate brine. [6, 10], in teir study of permeability damage via fines migration in extracted core material, concluded tat permeability and oil recovery were nearly independent of brine composition. Contrarily, oter experimental studies, suggested tat canges in brine composition could ave a large effect on oil recovery. [8] proposed tat additional oil recovery is te consequence of clay/clay interaction weakening in te porous medium [especially kaolinite] wen low salinity brine is injected. Tey consider tat te expansion of clay layers leads to detacment from te rock surface of mixed-wet clay particles tat are able to transport adsorbed oil droplets. Tis mecanism suggests a permeability reduction due to pore constrictions and/or fines production and evolution to a more water wet system. Oil deposits do not only contain oil, salt water, and often also free gas, is always present, too. Consequently, tere are witin te porous rocks at least two, possibly even tree, not into one anoter soluble pase, eac of tem influencing te flow capacity of te oter. Te permeability for eac pase is called te effective permeability. It is quite obvious tat te

3 70 Alabi O. O. and Sedara S. O: Evaluation and Accurate Estimation from Petropysical Parameters of a Reservoir effective permeability is no mere rock property any longer. It is affected not only by te rock, but also by te quantitative proportion of pases in te rock pores [1, 7, 10]. 3. Materials and Metods Te petropysical data were obtained from te quantitative analysis of seismic data using well log information. Te data for porosity( ϕ ), water saturation ( S ) and tickness ( ) were obtained from tree (3) wells in te same oil field. Te wells are tagged well 1, well 2 and well 3. ell 1 as five (5) zones wile well 2 and well 3 as tree (3) zones eac Computation of Correct and Incorrect Estimation and Relative Factor Te accepted formula for computation of Petropysical parameter is tagged te Correct Estimation () formula wile te unaccepted formula is tagged Incorrect Estimation (I) formula. Tese formulae were used to compute te average porosity, water saturation and ydrocarbon saturation. Te I formula always gives iger estimation tan te formula. Te ratio of te result obtained by I to is refers as Relative Factor (RF). Te Relative Factor (RF) is te measure of te magnitude of error in estimation for eac parameter Computation of Petropysical Properties Te correct estimation () formula for computation of te petropysical parameters are; Tickness ( ) Bottom Top (1) H S Ave S ϕ S ere; S is te ater saturation, ϕ is te Porosity, H is te Hydrocarbon saturation and is te Tickness. Te incorrect estimation (I) formula for computation of te petropysical parameters are; Average S Average H Average (φ) S (2) (3) (4) (5) (6) S (7) 4. Results and Discussion Te following data are given from an oil well were Table 1 sows te petropysical parameters obtained from te quantitative analysis of seismic data using well log information. Te data for water saturation ( S ), porosity ( ϕ ) and tickness () are presented for five (5) zones in te well. Zone Table 1. ell 1 petropysical parameters obtain from te reservoir. S ϕ ϕ S ϕ A B C D E Te computation of te average porosity, water saturation and ydrocarbon saturation by te correct formulae as done by equations 8, 11, and 14 respectively. Also tese same parameters were computed by anoter formula (Incorrect Formula) wic are equations 9, 12, and 15. Te Relative Factor (RF) is obtained by te ratio of te incorrect estimation (I) to correct estimation () i.e (I/). It is te measure of te magnitude of error in eac parameter Computation of Porosity : Average porosity( ) ϕ (8) I: Average porosity( ϕ) (9) Α Β C D E A+B+C+D+E So, (RF) I (10)

4 American Journal of Environmental Engineering and Science 2016; 3(2): Computation of ater Saturation : Average S I: Average S S S (11) (12) Α Β C D E A+B+C+D+E So, (RF) I Computation of Hydrocarbon Saturation : Average H I: Average H S S (13) (14) (15) Α Β C D E A+B+C+D+E So, (RF) I (16) Table 2 sows te results for petropysical parameters obtained by correct estimation () formula and incorrect estimation (I) formula and te relative factor vis-a-vis te magnitude of error obtained in eac parameter. Te results are obtained from five (5) zones (Table 1) from te well. Te result sows tat te magnitude of error in te computation of ydrocarbon saturation is iger tan oter petropysical parameters. Table 2. Summary of te petropysical parameter and relative factor (well 1). Parameters I RF Ave ϕ Ave S Ave H Table 3 sows te petropysical parameters obtained from te quantitative analysis of seismic data using well log information. Te data for water saturation ( S ), porosity ( ϕ ) and tickness () are presented for tree (3) zones in te well. Zone Table 3. ell 2 petropysical parameters obtain from te reservoir. S ϕ ϕ S ϕ A B C Te computation of te average porosity, water saturation and ydrocarbon saturation by te correct formula is done by equations 17, 21 and 23 respectively. Also tese same parameters were computed by anoter formula (Incorrect Formula) wic are equations 18, 21 and 24. Te Relative Factor (RF) is obtained by te ratio of te incorrect estimation (I) to correct estimation () i.e (I/). It is te measure of te magnitude of error in eac parameter Computation of Porosity : Average porosity( ) ϕ (17) I: Average ϕ (18)

5 72 Alabi O. O. and Sedara S. O: Evaluation and Accurate Estimation from Petropysical Parameters of a Reservoir Α Β C A+B+C So, (RF) I Computation of ater Saturation : Average S S I: Average S S Α Β C A+B+C So, (RF) I (19) (20) (21) 4.6. Computation of Hydrocarbon Saturation : Average H I: Average Hc_ sat S S (22) (23) (24) Α B C A+B+C So, (RF) I (25) Table 4 sows te results for petropysical parameters obtained by correct estimation () formula and incorrect estimation (I) formula and te relative factor vis-a-vis te magnitude of error obtained in eac parameter. Te results are obtained from tree (3) zones (Table 3) from te well. Te result sows tat te magnitude of error in te computation of water saturation is iger tan oter petropysical parameters. Table 4. Summary of te petropysical parameters and relative factor (well 2). Parameters I RF Ave ϕ Ave S Ave H Table 5 sows te petropysical parameters obtained from te quantitative analysis of seismic data using well log information. Te data for water saturation ( S ), porosity ( ϕ ) and tickness () are presented for tree (3) zones in te well. Zone Table 5. ell 3 petropysical parameters obtain from te reservoir. S ϕ ϕ S ϕ A B ater Computation of Porosity : Average porosity( ) ϕ (26) I: Average ϕ (27) Α

6 American Journal of Environmental Engineering and Science 2016; 3(2): Β C A+B+C So, (RF) I Computation of ater Saturation : Average S S I: Average S S Α Β C A+B+C So, (RF) I (28) (29) (30) 4.9. Computation of Hydrocarbon Saturation : Average H I: Average Hc_ sat S S Α Β (31) (32) (33) C A+B+C So, (RF) I (34) Table 6 sows te results for petropysical parameters obtained by correct estimation () formula and incorrect estimation (I) formula and te relative factor vis-a-vis te magnitude of error obtained in eac parameter. Te results are obtained from tree (3) zones (Table 3) from te well. Te result sows tat te magnitude of error in te computation of water saturation is iger tan oter petropysical parameters. Table 6. Summary of te petropysical parameters and relative factor (well 3). Parameters I RF Ave ϕ Ave S Ave H Conclusion e ave applied a new concept of computational analysis to examine te possible error in estimation of resources (ydrocarbon) in a reservoir. Te accuracy of te resource estimation depends on te correct computation of te reservoir properties from te well log data. e ave considered two sets of formulae for computation of te most important reservoir properties (parameters) for resources (ydrocarbon) estimation. For te same set of data from te same well in te same oil field, te results from te formulae demonstrate a significant deviation from eac oter. It is observed tat te magnitude of te error increase wit increase in number of zones in te well, Moreover te result sows tat in a well wit iger number of zones, te error is more associated wit te computation of ydrocarbon saturation, and tis could ave a significant cost effect in te estimation of resources. References [1] Alabi, O. O and Adeleke, A. E. (2014) Te effect of water salinity on permeability of oil reservoir. Proc. 2 nd Int. Conf. on Researc in Sci., Engr. and Tec. Marc 21-22, 2014, Dubai, pp [2] Asquit, G. B. and Krygowski, D. (2004), Basic ell Log Analysis for Geologists. AAPG Metods in Exploration. Tulsa, Oklaoma, No. 16. Pp [3] Asquit, G. B. (1991). Log Evaluation of Saly Sandstone Reservoirs: A Practical Guide: AAPG Course Notes Series,

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