Spectroscopy: The Key to Rapid, Reliable Petrophysical Answers

Transcription

1 Spectroscopy: The Key to Rapid, Reliable Petrophysical Answers Well-completion decisions require fast, objective and reliable log interpretation. A new, nearly automatic method for processing data from modern spectroscopy and conventional logging tools gives operators that information quickly. An extensive analysis of the relationship between rock properties and elemental concentrations in core samples provided a reliable basis for this new service. Dan Barson Rod Christensen OILEXCO Incorporated Calgary, Alberta, Canada Eric Decoster Caracas, Venezuela Jim Grau Michael Herron Susan Herron Ridgefield, Connecticut, USA Udit Kumar Guru Cairo, Egypt Martín Jordán Petróleos de Venezuela S.A. (PDVSA) Barinas, Venezuela Thomas M. Maher Apache Egypt Companies Cairo, Egypt Erik Rylander Clamart, France Jim White Aberdeen, Scotland For help in preparation of this article, thanks to Bill Batzer and Lisa Stewart, Ridgefield, Connecticut, USA; Matt Garber, Cambridge, England; Martin Isaacs, Sugar Land, Texas, USA; Daniel Valois, Barinas, Venezuela; and Richard Woodhouse, consultant, Surrey, England. DecisionXpress, ECS (Elemental Capture Spectroscopy), ELANPlus, GLOBAL, GLT (Geochemical Logging Tool), Litho-Density, MDT (Modular Formation Dynamics Tester), Minitron, Platform Express, RST (Reservoir Saturation Tool) and SpectroLith are marks of Schlumberger. Petrophysical interpretations comprising at least porosity and water saturation are essential for decisions about acquiring pressure data, collecting fluid samples, running casing and completing wells. Thus, providing reliable answers in real time or within a few hours after logging is of utmost importance for operators. Although reservoir characterization studies involve more data and time to fine-tune the interpretation for a particular reservoir, time and data are always in short supply. Even for these larger studies, a fast, reliable evaluation is useful as a starting point and as a convenient summary of the logs. Past attempts to provide a generalized interpretation package have been plagued by the need to manually define numerous parameters and formation-zonation levels. Ideally, these are selected by a skilled interpreter, or by referencing an established local database for the reservoir or formation. Unfortunately, neither of these options may be available at the time required. An alternative approach is to determine many of these parameters automatically. Now, elemental-concentration logs and the SpectroLith lithology processing of spectra from neutron-induced gamma ray spectroscopy tools make it possible to estimate all matrix parameters automatically with at least the same accuracy as conventional, time-consuming techniques. The number of parameters is dramatically reduced, in the optimal case to just one: the formation water resistivity, R w. At the same time, more rigorously scientific conductivity and permeability models improve the reliability of the results. The combination of these techniques, known as the DecisionXpress petrophysical evaluation system, has been applied successfully in a wide variety of siliciclastic reservoirs. For the time being, this system is not applicable to carbonate reservoirs, mainly because of the lack of good scientific models and the difficulty of distinguishing calcite from dolomite in the presence of gas. This article explains the basis for the algorithms that allow the DecisionXpress system to be fast, accurate and reliable, and shows examples from several different environments, such as Egypt, Venezuela and the North Sea. First, however, it is useful to consider the limitations of conventional techniques. The Log-Evaluation Problem A large part of well-log evaluation involves volumetric analysis. If the porosity and fluid saturations are known, the determination of fluid volumes is trivial. Matrix permeability cannot be measured directly by static log measurements, but it can be estimated from fluid and mineral volumes. The difficulty with volumetric analysis is that there are far more unknowns than measurements. In addition to gas, oil and water which can vary widely in composition, density and relative abundance from top to bottom of a hydrocarbon column 14 Oilfield Review

2 there are many possible mineral components. The log analyst also wants to know fluid mobility, for example whether the water in the formation is irreducible or is free to produce. 1 Modern logging suites can provide hundreds of measurements, but these measurements are not all independent. For example, many measurements respond strongly to porosity, but none uniquely identifies the volume of oil. Confronted with this challenge, the log interpreter is obliged to work with models that reduce the number of unknowns to an appropriate quantity for the measurements available. For example, if a reservoir is known to be a sandstone containing oil, the interpreter can exclude anhydrite and gas from the model. The interpreter must choose the model, so human intervention is required from the very beginning. Although this choice may be simple for an adequately developed reservoir, it is likely to be difficult in an exploration or appraisal well, or when the set of measurements changes from those obtained in offset wells that were used to establish the model. Models contain parameters expressing the response of the measurements to their components. Some parameters are precisely defined, for example the density of calcite. Some responses vary widely, such as the gamma ray response to shale. At this point, log interpretation programs take different approaches. Those that emphasize ease and speed of use employ simple models and allow only a few of the most variable parameters to be set by an interpreter. Those emphasizing accuracy offer complex models and allow most parameters to be modified by the user (see A History of Log Interpretation Methods, next page). Whatever the approach, the most difficult parameters to select are invariably those of clay minerals. Clay type, volume and distribution strongly affect the determination of porosity from porosity logs, such as neutron, density and sonic logs, and of water saturation from resistivity logs. In conventional log interpretation based on a triple-combo logging suite resistivity, density porosity, neutron porosity, gamma ray and spontaneous potential the volume of clay is determined mainly from the gamma ray response and the neutron and density measurements. The subjectivity of gamma ray (continued on page 18) 1. Irreducible water saturation is the lowest water saturation, S wirr, that can be achieved in a core plug by displacing the water with oil or gas. This state is usually achieved by flowing oil or gas through a water-saturated sample, or by spinning it in a centrifuge to displace the water with oil or gas. Summer 25 15

3 A History of Log Interpretation Methods Log interpretation techniques have progressed from the linear solutions of simple equations in the 194s to today s mathematical inversions and neural networks (right). 1 The development has been driven not only by improvements in computer technology, but also by the increasing number of well-log measurements and the improved understanding of log responses. The main goals of log interpreters to determine porosity, water saturation and permeability have not changed. What has changed is our ability to estimate these quantities more quickly and reliably in a wider range of formations, and to compute other outputs, such as irreducible water saturation and mineralogy. The foundation of quantitative log interpretation is the set of relationships introduced by G. E. Archie in At that early stage, interpretation was a sequential process first determine porosity from a sonic, neutron or density log, and then find water saturation using the resistivity log. This process was accomplished with charts and nomograms, which became increasingly complicated as more porosity logs became available and as the effects of clay and of the invaded-zone fluids were recognized and quantified. Log interpretation was no longer a simple sequential process, but one with many options and iterations. Such iterations were no problem for the calculators and computers being introduced at that time. By the end of the 196s, complex programs, such as the SARABAND system, could use all existing log measurements, estimate clay volume from a variety of sources and calculate the fluid saturations in both the invaded and uninvaded zones. 3 These programs operated sequentially, for example first estimating clay volume, then porosity and finally water saturation, but iterated extensively to refine the answer. Programs were tailored for particular types of formations, for 194s to 195s 196s to 197s 198s to 199s 199s to 2s Charts and Nomograms Step-by-step, manual process Simple models Overlays Curves presented on selected scales and read with transparent rulers Sequential or Deterministic Methods Complex logic with iterative loops Limited model flexibility Simultaneous or Statistical Methods Constrained inversion through minimization of uncertainty Greater flexibility in model Neural Network Inputs mapped to outputs based on training datasets Implicit model (inputs, outputs) DecisionXpress System Nearly automatic lithology and hence other outputs Few parameters Simple models Few or no parameters Explicit parameters: few or many Usually many explicit parameters Minimal number of parameters Implicit model Minimal number of parameters > The development of interpretation methods for multiple logging tools since the 194s. Single logging-tool interpretation is not shown. 16 Oilfield Review

4 example shaly sands, and for particular logging measurements and response equations. However, the complexity of the logic made it increasingly difficult to add a new measurement or interpretation idea later. In the late 197s, the idea of treating log interpretation as a problem of mathematical inversion was introduced. 4 Each measurement has a response equation that can be expressed as a set of unknown formation volumes, each multiplied by a parameter. When there are at least as many equations as unknown volumes, the latter can be found by common inversion methods. The solution can be constrained, for example by not allowing the porosity to exceed a specified amount, and each response equation can be given a different weight. In this way, the logic in the sequential programs could be mimicked, but it was not necessary to rewrite the software to add or subtract a measurement or a model. In the 198s, inversion methods were further developed and their computing time reduced to allow different models to be run simultaneously. 5 The most appropriate model could then be selected for each interval, either manually or using some automatic criterion. Whatever method is selected, the main tasks of computerized interpretation have remained the same. First, the input logs need to be edited, depth matched and environmentally corrected. These tasks are increasingly handled during acquisition, but still remain a concern in difficult conditions such as 1. For a detailed review: Marett G and Kimminau S: Logs, Charts, and Computers: The History of Log Interpretation Modeling, The Log Analyst 31, no. 6 (November December 199): Archie GE: The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics, Transactions of the American Institute of Mining and Metallurgical Engineers, 146. New York: American Institute of Mining and Metallurgical Engineers (1941): Introduced by Schlumberger in 197, the SARABAND system was the first computerized reservoir analysis. For more information: Poupon et al, reference 2, main text. washed-out boreholes. Second, the parameters and, where the option is available, the formation model, have to be chosen. Last, results must be checked for quality and the parameters or model changed until the interpretation is satisfactory. Parameter selection has always been a key subject in log interpretation. Manual parameter selection relies on selecting values from measurements (like mud-filtrate properties), logs, crossplots or histograms, for example by looking for the apparent water resistivity in a water zone. Unfortunately, there is no certainty that an interval is water-bearing that is an interpretation in itself. Manual parameter selection is often therefore a matter of judgment. Most automatic parameter-selection methods implement the logic behind a manual method, with the same limitations. In certain cases, inversion can determine parameters by making use of the fact that the parameters are constant over an interval. Finally, parameters can be chosen from databases specific to a particular reservoir, formation, geographical area or geological environment. These databases range from simple R w tables to sets of procedures and the experiences of experts. Quality control is even more subjective than parameter selection. Reconstructed logs those computed from the solution and the parameters and model used show whether the solution respects the input logs, but do not indicate whether the parameters or model are correct. In practice, the quality of the 4. Mayer C and Sibbit A: GLOBAL, a New Approach to Computer-Processed Log Interpretation, paper SPE 9341, presented at the SPE Annual Technical Conference and Exhibition, Dallas, September 21 24, Quirein J, Kimminau S, LaVigne J, Singer J and Wendel F: A Coherent Framework for Developing and Applying Multiple Formation Evaluation Models, Transactions of the SPWLA 27th Annual Logging Symposium, Houston, June 9 13, 1986, paper DD. result depends on the interpreter s judgment and comparison with other data, such as core analysis, well tests and production results. Experienced interpreters do not use software to find the solution, but rather to implement and refine ideas the interpreter gleans from studying the raw logs. However, this experience does not have to be general and take a long time to develop it can be obtained quickly in specific reservoirs or areas. More recent techniques minimize the problem of parameter selection. Artificial neural networks are trained to convert logs into results on wells where results are already known effectively finding internally the necessary transforms and parameters for the specific model and wells concerned. Once trained, the networks can be applied nearly automatically to other wells in which the same model applies. Although neural networks are most commonly used for lithology classification and for cases in which explicit transforms are not well-known, for example permeability estimation and reduced logging sets, they are also applied to volumetric analysis. Finally, the DecisionXpress system uses new measurements that allow some or all of the petrophysical properties to be determined nearly automatically. It is unlikely to displace other methods for detailed studies requiring high levels of accuracy and flexibility. It should, however, provide a significant improvement for initial, rapid decision making. JS Summer 25 17

5 interpretation is widely known and can be illustrated on almost any log example (below). Various techniques have been used to improve the estimation of clay volume. Some interpretation software uses the minimum of the clay volumes estimated by different methods, based on the reasoning that errors in each method always cause an overestimate. 2 This approach can minimize gross errors, but does not remove the need for accurate parameter selections. In other cases, the choice of models and parameters is often facilitated by using a database of knowledge about a particular reservoir, local area or type of geological environment to reduce the choices considerably and to minimize the need for human intervention. However, such databases might not be available until after an area has been developed. Invaded Formation Resistivity.2 ohm.m 2, Apparent Resistivity 1.2 ohm.m 2, Mudcake Apparent Resistivity 2 Washout.2 ohm.m 2, Bit Size Apparent Resistivity 3 6 in ohm.m 2, Caliper Apparent Resistivity 4 6 in ohm.m 2, Hole Deviation Apparent Resistivity 5 Photoelectric Factor -1 deg 9.2 ohm.m 2, 1 Cable Spontaneous Potential Invaded Zone Resistivity Thermal Neutron Porosity Tension -43 mv ohm.m 2, 45 % -15 lbf 6, Gamma Ray True Formation Resistivity Formation Density MD, ft gapi 2.2 ohm.m 2, 1.95 g/cm Shale 6,25??? 6,3??? > Log of a siliciclastic sequence, illustrating some of the difficulties of gamma ray interpretation. The gamma ray log, neutron-density separation and resistivity all clearly indicate shale above 6,246 ft [1,94 m] measured depth (MD). However at 6,296 ft [1,99 m] and below 6,348 ft [1,935 m], the gamma ray log indicates shale, but the other logs do not. Also, the minimum gamma ray reading of 3 gapi may or may not indicate pure clay-free quartz. An Express Solution During the last 2 years, new logging measurements have advanced interpretation. These improvements can be divided into two types those focused on a better definition of the fluids and those focused on a better definition of the solids. Direct fluid definition has been vastly enhanced by developments in nuclear magnetic resonance (NMR) logging tools. Since the main properties of interest porosity, saturation and permeability are fluid-related, NMR may appear to be the best option. There are limitations, however, particularly with saturation interpretations, because measurements are made in the invaded zone, close to the borehole, and because the NMR oil and water signals are sometimes not clearly separated. The other option is to define the volumes of solids and then apply familiar equations to determine the main reservoir properties from other measurements. For example, porosity can be determined accurately from the density log if the matrix density is known. Water saturation can be estimated from resistivity if clay conductivity and distribution are known. The DecisionXpress system follows this second option. 3 Its solution is based on measuring the concentration of some of the elements in rocks and then estimating the major matrix properties from these concentrations. Measuring elemental concentrations is not new: chemical elements have been detected with pulsed neutron spectroscopy logging tools since the late 197s, and concentrations were specifically derived for openhole formation evaluation by the GLT Geochemical Logging Tool in the mid- 198s. 4 Unfortunately, the GLT system was not widely used for several reasons: The GLT toolstring was long, operations were slow and therefore costly, the tool was not combinable, and the interpretation was complex. The recently introduced ECS Elemental Capture Spectroscopy sonde is short, simple to use and fast to run, and it measures sufficient elements to evaluate the lithology (next page, top). At each depth level, the processing flows linearly, starting with the computation of lithology, including clay volume, and proceeding through grain density, porosity, permeability and saturations (next page, bottom). The entire computation can be performed in real time, while logging, and in most situations with the selection of only one parameter, R w, which is often known in developed reservoirs. Because the output provides all log-derived parameters needed 18 Oilfield Review

6 1986 GLT Geochemical Logging Tool Telemetry cartridge Neutron detectors Low-energy neutron source 1991 RST Reservoir Saturation Tool Far detector Detector acquisition cartridge 1996 ECS Elemental Capture Spectroscopy Sonde < Spectroscopy logging tools for lithology determination. Each tool has at least one source that emits high-energy neutrons into the formation, and one detector that measures the gamma rays emitted by the reactions of neutrons with elements in the formation. The early GLT Geochemical Logging Tool also incorporated natural spectral gamma ray and aluminum activation measurements; it was long and slow to log, and it was not combinable with conventional logging tools. The RST Reservoir Saturation Tool was designed for cased-hole evaluation, and can also provide inputs to the SpectroLith technique. The ECS Elemental Capture Spectroscopy sonde is the optimal spectroscopy tool for openhole lithology and matrix property determination using the SpectroLith technique and other associated techniques. Neutron detectors Near detector AmBe source 7 ft long 6 sondes 2 sources 2 passes <6 ft/h Minitron source 36 ft long Through tubing [1 11 /16-in. or 2 1 /2-in. outside diameter (OD)] Minitron source <2 ft/h Detector Boron sleeve Electronics Dewar flask Heat sink 15 ft long 5-in. OD (with boron sleeve) Chemical source 1,8 ft/h 2. Poupon A, Clavier C, Dumanoir J, Gaymard R and Misk A: Log Analysis of Sand-Shale Sequences A Systematic Approach, Journal of Petroleum Technology 22, no. 7 (July 197): Herron MM, Herron SL, Grau JA, Seleznev NV, Phillips J, El Sherif A, Farag S, Horkowitz JP, Neville TJ and Hsu K: Real-Time Petrophysical Analysis in Siliciclastics from the Integration of Spectroscopy and Triple-Combo Logging, paper SPE 77631, presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 29 October 2, Hertzog R, Colson L, Seeman O, O Brien M, Scott H, McKeon D, Wraight P, Grau J, Ellis D, Schweitzer J and Herron M: Geochemical Logging with Spectroscopy Tools, SPE Formation Evaluation 4, no. 2 (June 1989): Total porosity, φ T Elemental concentration logs (Si, Ca, Fe, S, Gd, Ti) Density, neutron and resistivity logs SpectroLith lithology Density and neutron matrix properties Permeability, k Effect of clay on conductivity, Q v Total porosity, φ T Irreducible water saturation Water saturation, S w Relative permeabilities and water cut Reservoir volumes User-selected parameters: Switches for anhydrite and feldspar level Clay cation exchange capacity Formation water salinity Cutoffs for permeabilities and water cut > Processing flow and user-selectable parameters in DecisionXpress processing. Blue rectangles represent input data, green rectangles represent output data, and yellow rectangles indicate intermediate computations. The straightforward nature of the process contributes to its robustness. Summer 25 19

7 Capillary-Bound Water Pyrite Water Free Water Carbonate Pay Hydrocarbon Water Hydrocarbon Quartz/ Feldspar/ Mica Cable DecisionXpress Water Cut Tension Mineralogy Hydrocarbon 1 Moved Hydrocarbon Clay 1 lbf 5, Gamma Ray Flow Profile Intrinsic Permeability Porosity Volume LPKSR MD, ft gapi 2 1 1, md.1 5 % % 1 6,25 6,3 6,35 > DecisionXpress output for the previous log (page 18 ). A light gray mask indicates log intervals in which the input data are of poor quality because of hole conditions or other problems. The porosity, permeability and fluid saturations are summed and averaged over the pay interval using cutoffs on permeability and water cut selected by the user. These can also be presented in a table. Qualitycontrol flags in the far right track indicate the interpretation description for lithology (L), porosity (P), permeability (K), saturation (S) and relative permeability (R); green indicates a favorable interpretation, yellow means a moderately favorable interpretation and red reflects an unfavorable interpretation. The shale interval above 6,24 ft [1,92 m] is largely affected by poor hole conditions. 5. Sedimentary minerals contain single or multiple oxides. Even clay minerals can be treated as complex mixtures of oxides. Concentrations are expressed in percent by weight, because it is the mass and not the volume of an element that contributes to the measured yield. 6. The oxide closure model as applied to the ECS tool can be expressed as: F {X i *Y i / S i } = 1, where F is the unknown normalization factor, Y is the measured relative yield, X is the known oxide association factor, and S is the known relative detection sensitivity. The summation is over the six measured rock-matrix elements, designated Clay-Bound Water Porosity Siderite Anhydrite by variable i. Once F has been calculated at each level, the percent by dry weight, or elemental concentrations, are computed from W i = F * Y i / S i. Herron SL: Method and Apparatus for Determining Elemental Concentrations for Gamma Ray Spectroscopy Tools, US Patent No. 5,471,57 (November 28, 1995). 7. Herron MM, Matteson A and Gustavson G: Dual-Range FT-IR Mineralogy and the Analysis of Sedimentary Formations, paper 9729, presented at the Annual Society of Core Analysts Conference, Calgary, September 7 1, for picking points to measure pressure, fluidsampling intervals, and sidewall coring locations, it is crucial in making completion decisions (left). Some conventional analysis packages can also claim speed and automation once the analysis is adapted for specific environments. The key difference of this new technique is that it gives an accurate and reliable result in most siliciclastic reservoirs anywhere in the world. To justify this claim, we will examine the basis on which its algorithms are built. Elemental concentrations Spectroscopy tools like the ECS sonde actually measure a gamma ray spectrum, or the number of gamma rays received by the detector for each energy level. The gamma rays are produced when highenergy neutrons from a minitron or from a radioactive source such as americium [Am] and beryllium [Be] bombard the formation and lose energy through scattering, primarily by hydrogen. When slowed down to thermal energy, a neutron that collides with the nucleus of certain atoms can be captured; in this process, the nucleus is excited, and it emits gamma rays with a distribution of energies that is characteristic of the element. These gamma rays may be degraded by scattering in the formation and the detector, but there is sufficient character in the final spectrum to recognize the peaks caused by different elements (next page, top). The next step is to calculate the proportion, or relative yield, of gamma rays due to each element. To do this, the measured spectrum is compared with the standard spectrum acquired by Schlumberger for each individual element in the Environmental Effects Calibration Facility in Houston. The spectrum is inverted to obtain the yields of the principal contributing elements. These include some of the most diagnostic and abundant elements in sedimentary rocks, in particular silicon [Si], calcium [Ca], iron [Fe] and sulfur [S]. Titanium [Ti] and gadolinium [Gd] can also contribute significant signal and therefore must be solved for, even though they are not abundant elements. The yields of these six elements, all of which arise solely from the rock matrix, are computed and used quantitatively in further processing. Other elements, such as hydrogen [H] and chlorine [Cl], are also measured but used only qualitatively. 2 Oilfield Review

8 The yields are only relative measures because the total signal depends on the environment and varies throughout the logged interval. To obtain the absolute elemental concentrations, we need additional information in this case from the principle of oxide closure. This principle states that a dry rock consists only of a set of oxides, the sum of whose concentrations must be unity. 5 If we can measure the relative yield of all the oxides, we can calculate the total yield and the factor needed to convert it to unity. This normalization factor will then convert each relative yield to a dry weight elemental concentration. In practice, this process is not so straightforward. First, we measure elements, not oxides, but nature is helpful since the most abundant elements exist in only one common oxide, for example SiO 2 for silicon. Thus, for most elements, an exact association factor supports conversion of the concentration of the element to the concentration of the oxide. Second, although the ECS tool measures a majority of the most common elements, there are exceptions, the most important being those of potassium [K] and aluminum [Al]. Fortunately, the concentration of these elements is strongly correlated to that of iron, so that they can be included in the oxide association factor for iron. 6 The results have been validated by comparison to chemical concentrations measured on core samples (below right). Elements to minerals The next step is to convert elemental concentrations into mineral groups. Earlier geochemical techniques were designed to determine as many minerals as possible. In this DecisionXpress technique, the primary goal is an accurate and reliable total clay content or weight fraction, with remaining minerals being divided into carbonates or into quartz, feldspars and micas (QFM). Development of this technique was based on the study of more than 4 core samples from different sand and shaly sand environments. Each sample was crushed, mixed and split into two fractions one to determine elemental concentrations through chemical analysis, the other to determine mineralogy using the Fourier transform infrared (FT-IR) procedure. 7 The mineral standards for the FT-IR procedure included 26 minerals, all of which can be determined with an accuracy that exceeds +/- 2% by weight. Depth, ft Number of gamma rays detected, counts per second H Inelastic Gd Si Gamma ray energy, measurement bin number > Typical gamma ray spectrum from the ECS tool in a siliciclastic environment that has no calcium or sulfur. The thermal neutron capture gamma rays are shown divided into the contributions of the different elements present. Gamma rays from inelastic neutron reactions are also present, but are not used quantitatively. The capture yields of iron [Fe] and calcium [Ca] include small signals from aluminum and sodium. This contamination is taken into account during further processing Silicon, % by weight Calcium, % by weight Iron +.14 Al, % by weight Sulfur, % by weight Titanium, % by weight Gadolinium, ppm > An example of the good agreement between six elemental concentrations measured on core (red) and those derived by applying the oxide closure principle to the yields of the ECS tool (black). The iron yield contains some signal from aluminum, so that it actually measures the iron concentration plus 14% of the aluminum concentration. The core data (red circles) are plotted using the same combination. Cl Fe Elemental concentration, dry weight fraction Summer 25 21

9 Clay, % by weight 1 5 Clay, % by weight Clay, % by weight Clay, % by weight Thorium, ppm Aluminum, % by weight Silicon, % by weight 5 1 Uranium, ppm 1 2 Titanium, % by weight 15 3 Iron, % by weight Potassium, % by weight 5 1 Gadolinium, ppm 2 4 Calcium, % by weight > Comparison of the concentrations of various elements measurable by logs with measured clay concentration in one well. The top row contains elements measured by natural gamma ray spectroscopy. The two trace elements, Ti and Gd, and the three major elements measured by capture gamma ray spectroscopy are shown in the middle and bottom rows, along with aluminum, which is difficult to measure with wireline or logging-whiledrilling tools. As observed in many wells, there is a good correlation with aluminum and a good anticorrelation with silicon. In this well, the correlation with potassium is good, but this was an exception among the wells studied, particularly at low clay concentration. a 5 1 b SiO 2 1 SiO 2 CaCO 3 MgCO 3 1 SiO 2 CaCO 3 MgCO Fe > Data from 12 wells illustrating how clay concentration is estimated from major elements. The measured clay concentration shows a clear trend with (1 SiO 2 ) that is disturbed mainly by carbonate minerals (a). When calcite and dolomite are subtracted from the previous estimate, the tight trend is disturbed only by siderite and pyrite (b). When iron-rich minerals are also subtracted, the correlation is further improved, showing how clay can be estimated from four elements (c). In practice, magnesium is not measured by capture gamma ray spectroscopy, but the interpretation provides a total carbonate (calcite + dolomite) that effectively produces results identical to those shown in the middle plot (b). For complete lithology interpretation, dolomite can be estimated from the photoelectric factor from the Litho-Density photoelectric density log or Platform Express integrated wireline logging tool measurements. c 5 1 The study first examined the correlation between total clay and a number of elements conceivably measurable with logs (left). Total clay is the sum of the kaolinite, illite, smectite, chlorite and glauconite fractions. In most wells, aluminum gives the best correlation, which is not surprising because clays are aluminosilicates, and aluminum is an integral part of their chemical composition. Potassium sometimes correlates strongly when the dominant clay is illite, but the correlation is perturbed by potassium in feldspars, micas and other minerals. Thorium [Th], uranium [U], titanium [Ti] and gadolinium [Gd] are trace elements that are often enriched in shales, but these elements do not generally reveal a sufficiently reliable correlation for quantitative use, primarily due to nonclay sources. Silicon shows a strong anticorrelation, decreasing from 46.8% by weight in pure quartz to about 21% by weight in clays. Iron is associated with heavy minerals, such as siderite and pyrite, and the clay minerals illite, chlorite and glauconite. Calcium occurs primarily in calcite and dolomite. Aluminum is the best single elemental indicator of clay, but it is difficult to measure in the borehole. Because of its small capture cross section, aluminum does not produce sufficient capture gamma rays to make a statistically reliable measurement. In the past, aluminum was measured by inducing neutron activation, a technique that required complex hardware, such as that in the GLT tool. For this reason, researchers looked for other methods with better statistical precision to estimate total clay. The silicon anticorrelation is good, but is disturbed by the presence of carbonate minerals, siderite and pyrite (left). These minerals act like clay to reduce the amount of silicon, but can be accounted for by measuring calcium, iron and, when available, magnesium [Mg], whose measurement is discussed below. Thus, by combining four elements Si, Ca, Fe and Mg it is possible to find a correlation with total clay that has nearly the same slope in all wells, a small degree of scatter, and a near-zero intercept (next page, top). When examining these plots, it is important to focus on the clay-poor region where reservoirs occur the correlation in the shales is less important. With the exception of Wells 11 and 12, discussed below, these results show a strong, unique correlation between elemental concentrations and total clay in a wide range of siliciclastic reservoirs. 8. Ellis DV: Well Logging for Earth Scientists. New York: Elsevier (1987): Oilfield Review

10 At this point, it is worth examining the correlation between measured total clay and the traditional total gamma ray on the same data (below right). 8 The gamma ray is calculated from the sum of its contributing elements K, Th and U and is therefore independent of porosity. As expected, there is a general correlation. However, the slopes and offsets vary widely, and there is often considerable scatter, particularly in comparison with the estimation based on elemental concentrations. Wells 1 and 2 illustrate the wide range in slope. An extrapolation to pure clay would give a gamma ray reading of 1 gapi in Well 1, but would give 5 gapi in Well 2. Wells 4 and 12 illustrate the range in offsets, or zero clay readings. An extrapolation to zero clay gives 3 gapi in Well 4 and 7 gapi in Well 12. Such variations are widely known and are partially circumvented in practice by using local knowledge and calibrating the gamma ray to core data in a particular reservoir. Calibration would give good results in several of the wells. However, the results are still not satisfactory in terms of scatter and dynamic range. In Wells 3, 5, 7 and 9, the scatter at about 2% by weight clay is such that even a calibrated gamma ray would indicate clay percentages varying from to 4%. This amount of clay can mean the difference between reservoir and nonreservoir rock, and makes quantitative use difficult. Wells 11 and 12 are examples of small dynamic range. Wells 11 and 12, and to a lesser extent Well 4, contain feldspar-rich sands. Feldspars and micas are aluminosilicates, like clays, and therefore affect the silicon content. These sands are handled by using a different slope and introducing an offset in the clay estimator (below). The 1 Clay, % by weight Clay, % by weight Clay, % by weight Well 1 Well 2 Well 3 Well 4 Well 5 Well 6 Well 7 Well 8 Well 9 Well 1 Well 11 Well Estimated clay, % 5 1 Estimated clay, % 5 1 Estimated clay, % 5 1 Estimated clay, % > Comparison of measured clay concentration with the concentrations estimated by Si, Ca, Fe and Mg in 12 wells. Except in Wells 4, 11 and 12, the slopes are nearly the same and pass through the origin with no offset. The overall correlation coefficient is.94, with a standard error of 6.9% by weight. In reservoir rocks that contain less than 25% clay, the standard error is smaller. The clay content tends to be underestimated in the shales; this underestimation is corrected in the SpectroLith implementation. Clay, % by weight Clay, % by weight Well 1 Well 2 Well 3 Well 4 Well 5 Well 6 Well 7 Well 8 Clay, % by weight 5 Clay, % by weight 1 5 Well 9 Well 1 Well 11 Well Estimated clay, % > Comparison of measured clay concentration with the concentrations estimated by Si, Ca, Fe and Mg in Well 4 (crosses) and Wells 11 and 12 (open circles) using the equation for arkosic, or high feldspar, sands. The correlation is strong, particularly below 2% clay Gamma ray, gapi Gamma ray, gapi Gamma ray, gapi Gamma ray, gapi > Comparison of measured clay concentration with gamma ray in the same 12 wells as in the previous figure (above). The gamma ray has been computed from the thorium [Th], uranium [U] and potassium [K] concentrations measured on the samples using the formula: gamma ray = 4Th + 8U + 16K, where Th and U are reported in parts per million (ppm), and K is in % by weight. This is equivalent to using a gamma ray log normalized to the solid fraction, or porosity-free. The slopes and offsets vary widely from well to well. Even after allowing for these, the correlations are poorer than when estimating using Si, Ca, Fe and Mg, especially in reservoir rocks. Summer 25 23

11 current DecisionXpress implementation has three different estimators corresponding to arenite (feldspar content < 1%), subarkose (feldspar content between 1 and 15%) and the rare arkose (feldspar content > 25%). Arenite is the default. The carbonate fraction is determined from the calcium concentration, initially assuming that the carbonate is calcite. Dolomite can be detected and quantified by comparing the expected photoelectric factor (PEF) with the measured PEF. 9 The fractions of halite, coal, Inversion (spectral stripping) Oxide closure siderite, anhydrite and pyrite are all measured using information in the different yields (below). The remainder of the rock is considered to consist of quartz, feldspar and mica (QFM). The extensive core studies helped scientists develop an accurate and reliable method of estimating clay from elemental concentrations without the need for user intervention. This process is captured in the SpectroLith algorithm. 1 One important advantage is that it uses concentrations of major elements, as opposed to trace elements that can be easily affected by sediment Induced gamma ray spectra Elemental relative yields (Si, Ca, Fe, S, Gd, Ti, H, Cl and other capture and inelastic yields and tool background) Elemental concentrations % by dry weight (Si, Ca, Fe, S, Gd and Ti) diagenesis, depositional environment or the spurious introduction of small amounts of heavy minerals. The results are demonstrably superior to those from the gamma ray log even when the gamma ray analysis is calibrated with core. Also, unlike the lithology analysis using neutron and density porosities, the results are independent of fluid type, volume and density. Matrix properties and porosity In conventional log analysis, matrix density is either taken as a constant based on local knowledge or is derived from mineral modeling. The former is likely to be approximated, leading to errors, while the latter involves analyst input and control. An alternative approach is to estimate matrix density directly from elements. As with the lithology study, elemental concentrations and matrix densities were available on a large number of core samples, in this case more than 6. The goal was to find the best correlation between the matrix density and a linear combination of elements. Although the algorithm is empirical, its rationale is logical. 11 Sandstone matrix density is approximately equal to that of silica [SiO 2 ], but increases as the concentrations of calcium-, iron- and sulfur-bearing minerals increase. Iron-bearing minerals have a particularly strong effect on density, as reflected in the high coefficient for iron. A separate algorithm with different coefficients is used for arkosic sandstones. A similar analysis leads to an algorithm for the matrix response of the neutron log. Knowing the properties of the rock matrix and the fluid normally those of mud filtrate it is straightforward to calculate the total porosity from both neutron and density logs. In water zones, the matrix-corrected porosities should agree regardless of the volumes of clays or heavy Mineral Anhydrite, CaSO 4 Element Used Sulfur Comment SpectroLith model Pyrite, FeS 2 Sulfur User elects to solve for either anhydrite or pyrite. Corresponding weight % of Ca or Fe is subtracted from measured weight % before calculating other lithologies. SpectroLith lithology % by dry weight (clay, carbonate, QFM and special minerals) Properties such as φ T, ρ b, ρ clay, ρ carb and ρ QFM Lithology Volume % of rock, with fluids (clay, carbonate, QFM and special minerals) Siderite, FeCO 3 Coal, CH a N b O c Halite, NaCl Iron Hydrogen Total count rate above threshold From the iron remaining after computing pyrite and clay From excess hydrogen above average hydrogen level in well. Other minerals normalized to noncoal fraction If detected, lithology set to 1% halite > Overview of the SpectroLith algorithm. The processing flow (left) starts with capture yields and determination of lithology in % by dry weight. The lithology is subsequently converted to % by volume using porosity, log bulk density and the density of the mineral components. The table summarizes the logic used to detect special minerals and coal (above). 24 Oilfield Review

12 minerals. In gas zones, there should be a clear crossover, unmasked by the effects of clay. Finally, the total porosity, Ø T, for use in further computations, is taken as two-thirds of the density porosity, Ø D, plus one-third of the neutron porosity, Ø N. This expression yields an approximate but reliable estimate of Ø T for any formation fluid. Water saturation Many equations are available to compute water saturation from resistivity. Because we have a reliable measure of clay volume, it makes sense to select an equation that uses clay volume explicitly and is based on laboratory studies. The Waxman-Smits-Thomas equation satisfies these conditions and is the current choice in the DecisionXpress system. 12 The Waxman-Smits-Thomas equation contains the only two parameters that must be selected by the user formation water conductivity, C w, and the clay cation exchange capacity (CEC). Formation salinities vary far too widely for a fixed default to be satisfactory. The clay CEC default is.1 meq/g, a good value for most illites and chlorites, and a good value for most clay mineral assemblages the researchers encountered in sedimentary rocks; pure kaolinite clays and pure smectite clays are not present in the extensive database. In a water-filled formation, with S w = 1, the same equation is used to calculate the waterfilled formation resistivity, R o, and the apparent formation-water resistivity, R wa. k- permeability Permeability is calculated by a method developed for siliciclastic formations based on the lambda parameter,. 13 The lambda parameter is a measure of the effective diameter of dynamically connected pores, and, in the simplest pore geometries, it can be approximated from the ratio of pore volume to surface area. Further, at high permeability, the permeability is proportional to 2 /F, where F is the Archie formation factor and equal to 1/Ø 2. Combining these leads to an expression that is a form of the Kozeny-Carman relation and similar to many others in the literature: k ~ Ø m* / (S/V p ) 2, where S is the pore surface area and V p the pore volume. 14 The problem is then how to measure the ratio S/V p from logs, and how to adapt the equation at low permeability. In the mineral form of k- permeability, the ratio S/V p is estimated from the volumes of the minerals present. This is possible by first removing the effect of porosity in the ratio, leaving two terms, the Measured permeability, md k-λ estimate Measured porosity, % > Permeability calculation based on the lambda parameter,. Measured porosity and permeability values (blue) and the k- estimate (red) for clayfree Fontainebleau formation quartz arenites are shown at left. Measured permeability (blue) versus k- estimate (red) for the same formation appears at right. The correlation coefficient for the logarithms is.99. matrix density and the specific surface area per unit mass, S. 15 S is a characteristic of different types of minerals. It is known that clays have high S and make by far the largest contribution to pore surface area in shaly sands. It has also been observed that the total S in a rock can be approximated by a linear combination of the 9. PEF refers to a log of photoelectric absorption properties. The log measures the photoelectric absorption factor, P e, which is defined as (Z/1) 3.6, where Z is the average atomic number of the formation. P e is unitless, but because it is proportional to the photoelectric cross section per electron, it is sometimes quoted in barns/electron. Because fluids have low atomic numbers, they have little influence, so that P e is a measure of the rock matrix properties. The PEF of dolomite is less than that of calcite. The PEF reconstructed from the computed matrix fractions should equal the measured PEF if the carbonate is pure calcite. If the measured PEF is less, the difference is proportional to the fraction of dolomite. See Hertzog et al, reference Herron SL and Herron MM: Quantitative Lithology: An Application for Open and Cased Hole Spectroscopy, Transactions of the SPWLA 37th Annual Logging Symposium, New Orleans, June 16 19, 1996, paper E. 11. For arenites or subarkosic sandstones, researchers found a single least-squares fit with a correlation coefficient of.97 and a standard error of.15 g/cm 3 [.936 lbm/ft 3 ] as follows: ma = W Si W Ca W Fe W S, where W Si, W Ca, W Fe and W S are the % by dry weight of these elements. Herron SL and Herron MM: Application of Nuclear Spectroscopy Logs to the Derivation of Formation Matrix Density, Transactions of the SPWLA 41st Annual Logging Symposium, Dallas, June 4 7, 2, paper JJ. 12. The Waxman-Smits equation for the conductivity response of shaly formations is used to analyze core data and to calculate water saturation from resistivity and other logs. The model was developed by M. Waxman and L. Smits with later contributions by E. C. Thomas. The Waxman-Smits-Thomas equation may be stated as follows: 1/R t = C t = Ø T m* S w n* (C w + BQ v /S w ), where C t is conductivity, or the reciprocal of R t, the measured log resistivity; S w is the water saturation; m* is the cementation exponent and is a well-defined function of Ø T and Q v ; n* is the saturation exponent set to 2; and C w Measured permeability, md k-λ estimate Estimated permeability, md mass fractions of the minerals present. 16 This works well until the pore throats become blocked at low porosity and permeability. Empirically, it is found that when the initial k- estimate is less than 1 md, it must be reduced by a suitable function. The quality of the k- estimates can be judged from the examples (above). is the formation water conductivity. The first term is equivalent to the Archie equation in clean formations. The second term, BQ v /S w, represents the additional conductivity due to clay, where B is a parameter that is a function of temperature and C w. Q v, the cation exchange capacity (CEC) per unit pore volume, is directly related to clay volume and its CEC. CEC is the quantity of positively charged ions that a clay mineral or similar material can accommodate on its negatively charged surface, expressed as milli-ion equivalent per 1 g, or more commonly as milliequivalent (meq) per 1 g. Smits LJM and Waxman MH: Electrical Conductivities in Oil-Bearing Shaly Sands, Society of Petroleum Engineers Journal 8, no. 2 (June 1968): Waxman MH and Thomas EC: Electrical Conductivities in Shaly Sands I. The Relation Between Hydrocarbon Saturation and Resistivity Index; II. The Temperature Coefficient of Electrical Conductivity, Journal of Petroleum Technology 26, no. 2 (February 1974): Herron MM, Johnson DL and Schwartz LM: A Robust Permeability Estimator for Siliciclastics, paper SPE 4931, presented at the SPE Annual Technical Conference and Exhibition, New Orleans, September 27 3, Carman PC: Flow of Gases through Porous Media. London: Butterworth s Scientific Publications, The pore surface area, S, within a bulk volume V b, can be written as a product of the specific surface area per unit mass, S, and the mass of the matrix, which is its volume, (V b - V p ), multiplied by its density, ma. The porosity, Ø, is given by V b /V p. Thus, S/V p = S ma (1-Ø)/Ø. 16. On this basis, the initial k- estimate becomes: k 1 = 2, Ø (m* +2) / {(1-Ø) 2 ma 2 (6W clay +.22W sand + 2W carb +.1W pyr ) 2 }, where W clay, W sand, W carb and W pyr are the weight fractions derived previously, and the numerical coefficients are derived by fitting to experimental data. Theoretically, and also in practice, this expression does not apply at low permeability. When the initial k- estimate is less than 1 md, it must be reduced as follows: k 2 = k Summer 25 25

13 Western Desert and Nile Delta Generalized Stratigraphy A F R I C A Age Rock Unit Formation Unit Lithology Average Thickness, ft km 2 mi Alexandria East Bahariya Cairo E G Y P T Cretaceous Abu Roash Khoman A B A B C D E F G 1, , 2 Bahariya 95 > East Bahariya area, Egypt. Apache Egypt produces oil from the Cretaceous sandstones of the Bahariya and Abu Roash formations (right). Kharita Alamein 3, 16 Alam El Bueib 2, Irreducible water saturation To judge whether a reservoir will produce hydrocarbons, water or a mixture of both, it is not sufficient to know just the water saturation, S w. A qualitative judgment can be made by a simple comparison of S w with the irreducible water saturation, S wirr. If S w is equal to S wirr, then there is no producible water. More quantitatively, the effective permeabilities of oil, water and gas can be estimated using familiar relationships that depend on S w and S wirr. Irreducible water saturation is therefore an important parameter. In DecisionXpress processing, it is derived by the Coates-Timur equation. 17 This equation is normally used to estimate permeability, but may be inverted to give S wirr using porosity and the k- estimate of permeability: S wirr = 1 Ø 2 / (1 Ø 2 + k.5 ). With information on lithology, porosity, water saturation, permeability and irreducible water saturation, the operator has most of the major inputs needed to make reliable decisions. Let us now see the results of applying this logic to various sandstone reservoirs around the world. Rapid Evaluation of Complex Lithologies in Egypt In the East Bahariya concession onshore Egypt, Apache Egypt is drilling exploratory wells in Cretaceous sandstones of the Bahariya and Abu Roash formations (above). Geological uncertainty and abrupt changes in formation-water resistivity make rapid petrophysical analysis at the wellsite challenging but desirable. With two drilling rigs in operation, timely decision making is important to minimize the impact of these uncertainties on operations. The prospective Bahariya and Abu Roash sandstones tend to be thinly bedded and vary widely in grain size. Complex mineralogy, including glauconite, complicates log interpretation. 18 Apache expected that prompt, robust interpretations based on the DecisionXpress system would help geoscientists and engineers plan subsequent formation-evaluation operations, such as formation testing and fluid sampling with the MDT Modular Formation Dynamics Tester tool. Apache selected the DecisionXpress service in part because it integrates data from the Platform Express and ECS tools to determine mineralogy. This service also provides a continuous matrix density measurement that can be used in subsequent log processing. Apache validated the ECS-derived mineralogy of the Bahariya and Abu Roash formations with sidewall core analysis. The ECS data helped identify zones containing significant amounts of calcite. This was not possible using the standard PEF logs, which are affected by barite in the drilling mud. In a recent East Bahariya exploration well, EB-28, petrophysical evaluation using DecisionXpress technology compared satisfactorily with a conventional analysis carried out by Apache (next page). On the basis of this interpretation, Apache decided to run the MDT device to better understand fluid mobility and to collect fluid samples. The MDT permeability correlated well with permeability estimated 17. Timur A: Pulsed Nuclear Magnetic Resonance Studies of Porosity, Movable Fluid, and Permeability of Sandstones, Journal of Petroleum Technology 21, no. 6 (June 1969): Coates GR, Miller M, Gillen M and Henderson G: The MRIL in Conoco 33-1: An Investigation of a New Magnetic Resonance Imaging Log, Transactions of the SPWLA 32nd Annual Logging Symposium, Midland, Texas, USA, June 16 19, 1991, paper DD. 18. Glauconite is a silicate mineral found in sedimentary rocks. It typically forms on continental shelves characterized by slow sedimentation with organic matter present in an oxidizing environment. In sufficient quantity, it can form thick, sandy, green deposits. 26 Oilfield Review

14 Net Pay Porosity Net Reservoir Clay-Bound Water Siderite Mudcake Water Capillary-Bound Water Pyrite Perforated Intervals Washout Hydrocarbon Free Water Carbonate Bit Size Water Water Cut Hydrocarbon Quartz/ Feldspar/Mica 6 in. 16 DecisionXpress 1 Caliper Mineralogy Hydrocarbon Moved Hydrocarbon Clay Hydrocarbon Intrinsic 6 in. 16 Gamma Ray Flow Profile Permeability Porosity Volume MD, ft gapi , md.1 5 % % 1 LPKSR S w 1 % X,6 X,65 X,7 > Real-time petrophysical analysis of an East Bahariya well. This standard presentation displays borehole and depth information, red net pay flags and yellow net reservoir flags in the depth track. Track 1 shows lithology from the Platform Express integrated wireline logging tool. Perforations in four zones and the flow profile are shown in Track 2. However, the water cut information in Track 3 reveals a zone near X,675 ft that ultimately produced water. Fluid interpretations in Track 4 suggest that the best oil potential exists just below X,6 ft and around X,7 ft. The dominant minerals, shown in Track 5, include quartz, feldspar and mica (yellow) and clay (gray) with minor amounts of carbonate minerals (blue). Summarized in quality-control Track 6 are lithology (L), porosity (P), permeability (K), saturation (S) and relative permeability (R); green reflects a favorable interpretation, yellow means a moderately favorable interpretation and red indicates an unfavorable interpretation. Track 8 shows hydrocarbon volumes. Summer 25 27

15 Net Pay Net Water Reservoir Clay-Bound Water Hydrocarbon Mucake Capillary-Bound Water Water Cut Washout 1 Free Water Bit Size Water Intrinsic Hydrocarbon 6 in. 16 DecisionXpress Permeability Caliper Mineralogy Hydrocarbon 1, md.1 Moved Hydrocarbon 6 in. 16 Gamma Ray Flow Profile Mobility Porosity MD, ft gapi , md.1 5 % X,4 X,45 Porosity Siderite Pyrite Carbonate Quartz/ Feldspar/ Mica Clay Hydrocarbon Volume S w LPKSR % 1 % 1 using the DecisionXpress system (left). In addition, production results confirmed the DecisionXpress analysis. Apache employed DecisionXpress and ECS technology in other recent exploration wells drilled in two other concessions in Egypt for quick-look petrophysical evaluations to support wellsite decision making. Estimates of the ratio of net pay to gross pay from the DecisionXpress computation matched net-to-gross computations developed from time-consuming proprietary petrophysical analysis. DecisionXpress answers were typically available before production casing was run, which helped the operator estimate the value of exploratory wells and decide whether to run casing. Based on this success, plans are now being made to use the DecisionXpress software in real time to design more effective wireline pressureand fluid-sampling programs as well as to provide faster quick-look petrophysical analyses. X,5 X,55 X,6 X,65 X,7 X,75 > Permeability and mobility interpretations. The MDT device measured formation pressure and fluid mobility at nine depths within the Bahariya formation and at three depths in the overlying Abu Roash G zone (Track 3). Permeability calculated by real-time DecisionXpress processing (Track 3) closely matches MDT fluid mobilities. Real-Time Decisions in Venezuela The Guafita field is a mature oil field operated by Petróleos de Venezuela S.A. (PDVSA), located in the state of Apure, near the Venezuela Colombia border (next page, top). This field produces light oil, between 28 and 32 dapi, primarily from the Guardulio and Arauca members of the Guafita formation. In recent years, PDVSA initiated a sustained high level of drilling activity in the Guafita field to maintain high levels of production. With three active drilling rigs operating more than 3 km [186 mi] from PDVSA headquarters in Barinas, the company sought a reliable method for interpreting logs quickly at the wellsite. The PDVSA operations team in Barinas turned to induced gamma ray spectroscopy with the ECS sonde and the DecisionXpress system. The Guafita formation is a sand-shale sequence in which conventional log interpretation might be expected to be straightforward. In reality, several factors complicate log interpretation. First, the Guafita formation is highly resistive a clean sand will produce water at a resistivity of 1 ms/m [1 ohm.m], and oil at 3.3 ms/m [3 ohm.m]. This is because the formation connate water is unusually fresh, varying from a low of 1 parts per million (ppm) to a high of about 2,5 ppm of equivalent sodium chloride [NaCl]. Therefore, master calibration of the induction tool requires great care because the difference between 3 and 1 ms/m is significant, given that induction tools respond to conductivity, not resistivity. 28 Oilfield Review

16 This simplified resistivity approach is adequate for the clean sands, but it is inadequate when clay is present and surface conductivity effects become significant. At such low connatewater salinities, conditions in Guafita are outside the traditional range of application of conventional saturation equations such as the Waxman-Smits model. 19 In addition, the use of oil-base mud precludes acquiring a spontaneous potential curve, which in turn prevents the log analyst from using saturation equations designed specifically for freshwater environments, such as the Sen-Goode-Sibbit equation. 2 After analyzing this problem, PDVSA decided to focus on reducing the uncertainty associated with implementation of a conventional saturation model in Guafita. The company began with a thorough analysis of produced waters from various intervals in several wells to optimize the R w value to be used in each geological interval. At the same time, PDVSA recognized that a traditional estimate of clay content, always biased by the gamma ray log, tended to overestimate the amount of clay present in the formation and invalidated a saturation clay correction. The high radioactivity often observed in the Guafita sands is usually attributed to incompatibility between the original connate water and the water from the active aquifer below, which is fresh and likely to originate from meteoric recharge. As the aquifer rises, radioactive salts deposited in the formation increase the overall radioactivity, and lead to overestimation of clay content. In discussions with Schlumberger, PDVSA identified induced gamma ray spectroscopy with the ECS device as a potential means to accurately quantify clay in Guafita sands. In various Guafita wells, several runs of Platform Express integrated wireline logging tools, including the ECS sonde, systematically demonstrated poor linearity between natural gamma ray and clay volume, V clay, from SpectroLith processing. They also showed higher end points than are typical for sandstones, both for the clean and shaly fraction (right). 19. The Waxman-Smits equation is described in reference The Sen-Goode-Sibbit saturation model is commonly applied in freshwater shaly sand environments. For more information: Sen PN, Goode PA and Sibbit A: Electrical Conduction in Clay-Bearing Sandstones at Low and High Salinities, Journal of Applied Physics 63, no. 1 (May 15, 1988): Gamma ray, gapi Guafita field km 3 mi Apure 3 Caracas S O U T H A M E R I C A V E N E Z U E L A > Guafita field, Venezuela. Located in the Apure state near the Venezuela Colombia border, the field was discovered in 1984 and produces from Miocene and Oligocene reservoirs of the Guafita formation GR = * V clay Clay volume, % > Crossplot of gamma ray versus clay volume, as determined by SpectroLith processing. The regression line highlights the lack of linearity between measured gamma ray (GR) and clay volume, V clay. The end points of the regression line, at the intersection with the V clay = and V clay = 1 axes, are unusually high for a sandstone environment. These measurements were later used in DecisionXpress processing. Summer 25 29

17 Clay Irreducible Water Quartz/Feldspar/Mica Water Carbonate Oil Washout Bit Size 6 in. Caliper 6 in Gamma Ray Pyrite Grain Density from SpectroLith Processing 2.5 g/cm Grain Density from Core 2.5 g/cm 3 Clay Volume from XRD Coates-Timur Permeability 1, md 1, md Klinkenberg- Corrected Core Permeability.1 SDR Permeability.1 Irreducible Water Water Oil ELANPlus Fluid Analysis 5 % Core Porosity Calcite Pyrite Quartz Bound Water Illite ELANPlus Analysis T 2 Distribution T 2 LM 29 MD, ft gapi 3 % 1 1, md.1 5 % 1 1 %.3 ms 5, X,45 X,5 X,55 X,6 > SpectroLith processing of a Guafita log. Caliper measurements in the depth track show the hole is in good condition. Track 1, scaled from to 3 gapi, shows high gamma ray values in the logged interval. SpectroLith clay volumes (gray) shown in Track 2 match core measurements (blue circles); the SpectroLith grain density (red curve) is more reliable than the low core-density measurements (open circles) from unconsolidated core samples. Permeability estimates in Track 3 match those measured in cores (blue circles). Computed porosity (Track 4) also matches porosity measurements in cores (blue circles). Track 5 displays lithology and porosity from ELANPlus volumetric analysis. NMR data in Track 6 show a well-developed free-fluid signal in the highly permeable Guafita sandstones. Simultaneously, field results from DecisionXpress processing correlated strongly with results obtained using a traditional ELANPlus advanced multimineral log analysis technique in the Caracas, Venezuela, computing center. PDVSA found these results encouraging, and the company decided to verify them by acquiring a core and performing a series of X-ray diffraction (XRD) measurements for comparison with the clay volume determined by SpectroLith processing. This comparison reveals a good correspondence between total clay content determined by XRD analysis and clay content determined by SpectroLith analysis, even though the analyzed core plugs were obtained in the cleaner, more porous reservoir intervals (above). Agreement between estimated porosity and estimated permeability and core data was also excellent. Some discrepancy remains between grain density estimated from SpectroLith analysis and grain density measured in core samples, with core grain density typically lower than the density of pure quartz. This discrepancy might have resulted from the difficulty of accurately 3 Oilfield Review

18 Mudcake Washout Clay-Bound Water Capillary-Bound Water Clay Quartz/ Feldspar/ Mica Carbonate Pyrite Cable Tension Hydrocarbon Anhydrite 1, lbf Bit Size 6 in. 16 Calibrated Caliper 6 in. 16 Net Reservoir Net Pay DecisionXpress Mineralogy Water Hydrocarbon Flow Profile 1 Water Hydrocarbon k- Λ Permeability 1, md.1 Moved Water Siderite Free Water Coal Moved Hydrocarbon Salt Data Quality Data Quality Total Porosity Total Porosity 5 % 1 % LPKSR X,45 X,5 X,55 > DecisionXpress processing of the Guafita log. To better evaluate the quick-look results obtainable using DecisionXpress processing, the same interval of the previous log from Guafita (previous page) was reprocessed using the DecisionXpress system; this display is a default presentation. Caliper data in the depth track confirm hole quality was good except for rugosity from X,465 to X,47 ft. This thin interval of data, masked with gray, is not reliable enough for automated interpretation. Net reservoir and net pay flags are also shown in the depth track. Mineralogy from DecisionXpress processing appears in Track 1. Track 2 shows the estimated production profile, derived from the relative permeability results shown in Track 3. Porosity and fluid information in Tracks 4 and 5 complete the evaluation. Mineralogy, shown in Track 5, is interpreted from ECS data using DecisionXpress processing. Summarized in quality-control Track 6 are lithology (L), porosity (P), permeability (K), saturation (S) and relative permeability (R); green indicates a favorable condition, yellow represents a moderately favorable condition and red means an unfavorable condition. The quick-look DecisionXpress analysis agrees with core data and with the more timeconsuming ELANPlus analysis. measuring grain density from essentially unconsolidated core plugs. XRD analysis also showed the predominant clay mineral to be kaolinite, often greater than 7% of the total clay, with the rest of the clay minerals illite and a small fraction of chlorite. In such conditions, a low mean CEC of the clays would be expected; a value of.2 meq/g was used in the DecisionXpress processing (above). This quick-look result is remarkably similar to the complete ELANPlus evaluation, including the permeability estimate obtained from the mineralogical version of the k- equation. The rugose hole region from X,465 to X,47 ft is properly flagged, and the main sandstone reservoirs are properly diagnosed as being at or near irreducible water saturation. This was confirmed by production results, with the well coming on production at a rate of 1,2 bbl/d [191 m 3 /d] of fluid, with a water cut of less than 2%. DecisionXpress technology is now an integral part of formation evaluation in the Guafita field, ensuring that reliable interpretation results from a remote region of Venezuela are available wherever decisions need to be made, minutes after the logs are acquired. Summer 25 31

19 Block 15/25b N O R W A Y N o r t h S e a DENMARK UK km 2 mi 2 > OILEXCO license in the Outer Moray Firth basin. A thin oil column, identified in 199, led the company to reevaluate the potential of Block 15/25b. Making Timely Decisions in the UK In recent years, the UK government has encouraged owners of undeveloped offshore UK discoveries to either develop or relinquish acreage containing these discoveries. Many unappraised prospects have therefore been returned by the former owners and offered as new licenses. The availability of these licenses has attracted a number of operators new to the North Sea who saw economic potential in some of these relinquished blocks. One such operator was OILEXCO, a Calgary-based company that is currently developing Block 15/25b in the Outer Moray Firth basin. A thin oil column, discovered in 199, attracted the attention of OILEXCO (above). After reprocessing seismic data and mapping possible stratigraphic traps, the company initiated a multiwell drilling program. 21 To assist in understanding results from logging, the company used the DecisionXpress system. Of the three wells logged with the DecisionXpress system, Well 15/25b-8 proved to be the one that justified further activity in the area, known as the Brenda accumulation. That well was drilled on an anomalous amplitude variation with offset (AVO) elastic impedance response, and encountered a hydrocarbon column within the Forties sandstone of nearly 5 ft [15 m] (next page). 22 Timely petrophysical analyses using the DecisionXpress system facilitated the rapid decision making required to sidetrack or to run casing and test the wells. In addition, prompt analyses provided OILEXCO with important economic information to keep remote partners and other investors fully apprised of reservoir capacity and likely producibility. The resultant appraisal work confirmed that the Brenda accumulation may be one of the largest discoveries in UK waters in recent years, and development drilling using high-angle and horizontal wells will commence in January For more about exploration for stratigraphic traps by OILEXCO: Durham LS: Subtle Traps Become New Prey, AAPG Explorer 25, no. 8 (August 24): Amplitude variation with offset (AVO) refers to a difference in seismic reflection amplitude with a change in distance between shotpoint and receiver. AVO responses indicate differences in lithology and fluid content in rocks above and below the reflector. 23. For additional examples: Poulin M, Hidore J, Sutiyono S, Herron M, Herron S, Seleznev N, Grau J, Horkowitz J, Alden M and Chabernaud T: Deepwater Core Comparison with Answers from a Real-Time Petrophysical Evaluation, paper SPE 9134, presented at the SPE Annual Technical Conference and Exhibition, Houston, September 26 29, 24. Rasmus JC, Horkowitz JP, Chabernaud T, Graham P, Summers M and Wise D: A New Formation Evaluation Technique for the Lower Tertiary in South Texas Predicting Production in Low Permeability, Fine-Grained Sandstones, paper SPE 969, presented at the SPE International Petroleum Conference, Puebla, Mexico, November 7 9, Oilfield Review

20 Mudcake Washout Clay-Bound Water Capillary-Bound Water Clay Quartz/Feldspar/Mica Carbonate Pyrite Cable Tension 1, lbf Bit Size 6 in. 16 Calibrated Caliper 6 in. 16 Net Reservoir Net Pay DecisionXpress Mineralogy Environmentally Corrected Gamma Ray gapi 2 Water Hydrocarbon Flow Profile 1 Water Hydrocarbon k- Λ Permeability 1, md.1 Hydrocarbon Moved Water Free Water Moved Hydrocarbon Data Quality Total Porosity 5 % Anhydrite Siderite Coal Salt Data Quality Total Porosity LPKSR 1 % X,1 X,15 > Petrophysical analysis of Well 15/25b-8. The net pay flags in the depth track of this DecisionXpress display reveal nearly 5 ft of oil pay near X,15 ft. Track 1 presents the gamma ray curve and lithology determined by the DecisionXpress system. Track 3 shows hydrocarbon and water along with intrinsic permeability. Fluid saturations and porosity are shown in Track 4. Detailed mineralogy, presented in Track 5, is determined using the ECS sonde and DecisionXpress processing. As in all DecisionXpress presentations, a gray mask indicates that results are outside tolerance specifications. Interpretation in Real Time The DecisionXpress system has been applied successfully to a wide range of siliciclastic reservoirs. 23 This real-time interpretation is not fully applicable to carbonate reservoirs, largely due to the lack of a universally accepted, robust saturation-evaluation scheme. On the other hand, the lithology and matrix property components of the system can bring significant improvement to carbonate evaluations, and they will be implemented in the future. By limiting the number of parameters selected by the log interpreter, automated interpretation minimizes interpretation bias. As with any effort to automate tasks ordinarily performed by people, automated interpretations must be carefully compared with other data to ensure valid results. Comparisons of log data to core and production data are critical for operators to use this technology, but with time, well-to-well comparisons should prove adequate to validate interpretations. The algorithms in the DecisionXpress system yield fast and reliable petrophysical interpretation. By understanding how much hydrocarbon is present, and whether it can be produced economically, operating companies can better plan formation-pressure and sampling jobs, mechanical and percussion sidewall-coring jobs and formation-testing operations, or elect to run casing, drill ahead, or sidetrack. In addition, rapid petrophysical analysis supports long-term decision making, such as development of completion strategies, stimulation programs and other operations. JS/GMG Summer 25 33