Determination Of Water Saturation Research Articles

Determine Oil and Water Saturations in Preserved Source Rocks From 2D T1-T2 NMR

Two-dimensional (2D) T1-T2 NMR has been widely proposed as a method to determine the fluids present in unconventional source rocks. However, the assignment of the components in the 2D T1- T2 NMR spectra has so far been based on conceptual consideration and lacks rigorous experimental verification. Many assignment variations appeared in the literature and can be misleading. For example, large T1/T2 peaks in the 2D T1-T2 NMR were suggested to be fluid in small pores coupled with kerogen solid through dipolar relaxation. Our recent 500-MHz NMR relaxation experiments found that the dipolar relaxation between nanoconfined fluids and solid matrix is not big enough to support that large T1/T2 interpretation. The objective of this paper is to determine what quantitative data can be obtained using 2D T1-T2 NMR from source rocks with high confidence. We found the oil and water saturation can be accurately measured in preserved source rock plugs. Two-dimensional T1-T2 NMR data were collected on preserved core plugs from five wells of a source rock reservoir using an inversion-recovery Carr-Purcell-Meiboom-Gill (CMPG) pulse sequence on a 12-MHz NMR instrument. The acquired 2D data were inverted using an optimized inversion software, MUPen2D. We then obtain oil and water content in the plug from the 2D T1-T2 NMR using an in-house-developed NMR MATLAB app. The porosity of all the plugs was measured using a combined NMR and gas porosimetry (CNG) method. Oil and water saturation were also obtained using an industry-standard method developed by the Gas Research Institute (GRI) on the rock material close to where the plug was drilled. The oil and water saturations measured on the preserved plugs using 2D T1-T2 NMR and CNG are consistent with those from the GRI method using crushed rocks and an invasive cleaning procedure. The results show that oil and water saturations were accurately determined from 2D T1-T2 NMR on preserved source rock samples with high confidence. The NMR method is nondestructive and noninvasive and takes less than 4 hours on a preserved source rock plug, while GRI is destructive and invasive and can take several weeks for an accurate measurement on one sample. The measured results can be further used to determine the production potential of a source rock well in combination with log data.

Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight gas carbonate reservoirs in Bohai Bay Basin, China

Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

An Artificial Neural Network Model for Predicting Initial Water Saturation of Petroleum Reservoirs

Initial Water saturation is the water saturation of a reservoir before production commences. It enables the reservoir engineer to properly estimate the correct volume of Oil or gas reserves and to produce without water. And over the years over estimation or under estimation had caused major changes in the decision making of oil companies. New techniques are developed as technology advances to measure water saturation. These are the most widely used techniques for determining water saturation, nevertheless. Measurements obtained directly from a sealed core, which are more expensive, or calculations made using the Archie equation on sample well logs, which are less expensive. In this Project, Artificial Neural Network (ANN) model is the sole purpose of the modelling. The datasets are gathered, processed, trained, tested and validated.

New Insights on Water Saturation Determination of Carbonate Reservoirs Using Artificial Intelligence Approach (AIA) and Conventional methods

New Insights on Water Saturation Determination of Carbonate Reservoirs Using Artificial Intelligence Approach (AIA) and Conventional methods

Prediction of cementation factor using different methods of reservoir rock classification in heterogeneous carbonate reservoirs

Cementation exponent or factor is one of most crucial factors in the Archie’s equations that should be estimated precisely in order to accurately determine relevant petrophysical characteristics of the reservoir. Inaccurate estimation of this factor leads to incorrect petrophysical analysis and inaccurate determination of water saturation which is highly important for the economic forecasting of hydrocarbon reservoirs. An alternative to the cementation factor precise estimation is to classify rocks based on a common petrophysical property such as permeability. This research aims to investigate the cementation factor in different rock types and eventually to provide a suitable model for estimating this parameter based on different approaches of rock classification. In this paper, first, conventional methods of rock classification (e.g., current zone indicator (CZI), flow zone indicator (FZI), permeability and electrical efficiency) have been used for cementation factor estimation. Then, the data, taken from Regnet et al. (J Geophys Res Solid Earth 120:790–811, 2015), have been analyzed by these methods in order to find the best way of rock classification. The analyzed data show that only electrical efficiency method is accurate enough to classify rocks into different groups as high values of determination coefficient (i.e., 0.9828 and 0.9725) are obtained as a result of classification by this method in which the data are classified into distinct groups. The obtained values of determination coefficient as a result of classification by permeability, FZI and CZI methods are 0.805, 0.809 and 0.7568 respectively. Due to this result and high scattering of the classified data, it can be concluded that these three methods have not been precise enough for rock classification of the data. In addition to the above-mentioned existing methods, a new rock classification method based on tortuosity coefficient or factor has also been proposed in this paper. This new method has been proven to be accurate due to low scattering of the data results and falling them into distinct groups in addition to obtaining high values of determination coefficient (i.e., 0.9996, 0.9986 and 0.9951) in the classification by this new method.

Accurate determination of water saturation in tight sandstone gas reservoirs based on optimized Gaussian process regression

Tight sandstone gas is an important field for the future development of the oil and gas industry. In the tight gas reservoir of Penglaizhen Formation in Shifang gas field, the water saturation (Sw) of sealed coring cores and the NMR irreducible Sw of conventional cores are measured by using the reservoir physical property tester and NMR analyzer. Combined with the scanning electron microscope results, the reservoir has the characteristics of complex pore structure and high-water saturation. The calculation of Sw by Archie formula, Density log, and φD-φN has a large error. Therefore, how to accurately calculate Sw is an outstanding problem that needs to be resolved. In this paper, we selected AC, EDN, XI, and φe as the parameters for modeling. We have proposed the optimized Gaussian process regression (GPR) method to calculate the Sw of tight sandstone reservoirs. The water saturation curve calculated by the optimized GPR model is in good agreement with the core saturation value. In the error results of well data in the study area, it was found that the relative error and root-mean-square error of the optimized GPR model were the lowest, and it had better calculation accuracy. In addition, in the error results of the nuclear magnetic core samples, it was also found that the optimized GPR model's MMRE was 6.65% and the RMSE was 4.95. In tight sandstone reservoirs with complex pore structure and high-water saturation, the optimized GPR model is helpful to reduce the influence of reservoir shale characteristics, low resistivity characteristics and hidden information of parameter data during the calculation of Sw. It can calculate Sw more efficiently and reflect the characteristics of irreducible Sw more accurately. The optimized GPR model developed for water saturation calculation has broad application prospects.

A novel method for determining water saturation of porous media in relative permeability measurement using DPT

Relative permeability is essential for understanding porous media’s gas and water seepage characteristics and establishing production schedules in practical engineering applications. However, the movable water is too small to be detected in ultra-lowpermeability rocks, and it is difficult to determine the water saturation in the relative permeability measurement accurately. In this study, a differential pressure transducer (DPT) was applied to self-developed apparatus to quantify displaced water precisely. The results indicate that: (a) both the permeability and the relative permeability measurement results show high stability in repeatability tests with the application of DPT. (b) The final cumulative water flow data measured by the DPT is reliable; the relative error of the electronic balance and DPT value was less than 4%. (c) This self-developed instrument can obtain the relative permeability curve for ultra-low permeability rocks, such as tight sandstone and anthracite coal. Although there are limitations, this technique provides an economical and reliable pathway for studying the seepage characteristics of gas and water in ultra-low permeability rocks.

Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data

Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements.

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R2 of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient (R2) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.

A Review of Historical Studies for Water Saturation Determination Techniques

Water saturation is the most significant characteristic for reservoir characterization in order to assess oil reserves; this paper reviewed the concepts and applications of both classic and new approaches to determine water saturation. so, this work guides the reader to realize and distinguish between various strategies to obtain an appropriate water saturation value from electrical logging in both resistivity and dielectric has been studied, and the most well-known models in clean and shaly formation have been demonstrated. The Nuclear Magnetic Resonance in conventional and nonconventional reservoirs has been reviewed and understood as the major feature of this approach to estimate Water Saturation based on T2 distribution. Artificial intelligence has recently been used to predict water saturation and other parameters in the reservoir characterization process using seismic data, so the main idea of this technique and a list of the author's researchers have been reviewed. In this review article, the reference approach using core analysis by distillation-extraction and retorting techniques have been explained, as well as the saturation-height method, which is based on the capillary pressure concept and wettability. Finally, alternative experimental approaches based on scanning are expressed in this manner.

Petrophysical evaluation for development of a marginal oil field in the onshore Niger Delta, Nigeria

Petrophysical evaluation was conducted on an onshore marginal field in Niger Delta with the aim of evaluating the rock and fluid properties to boost hydrocarbon production in the field. Five well logs suite from five wells and core log data for two wells were utilized for this study. Petrophysical properties evaluated included; porosity, net to gross, formation factor, irreducible water saturation, permeability, water saturation, hydrocarbon saturation and pay thickness. The well logs suite contained the following logs: Gamma ray log for lithology identification; Resistivity log for fluid type discrimination and determination of water saturation; Density log for porosity determination; and Neutron log in combination with density log for hydrocarbon types. A total of seven reservoir sands (, Sand A, B, C, D, E, F and G) were identified and correlated across all five wells on the basis of gamma ray and resistivity log motifs. The reservoir gross thicknesses ranged from 62.55 to 228.50 ft, shale volume from 7.0 to 24.60%, net to gross from 0.76 to 0.93%, effective porosity from 20.78 to 26.22%, water saturation from 35.80 to 62.30% and permeability ranged from 545.94 to 2821.97 mD. This shows that the reservoirs are of good quality for hydrocarbon productionacross the field.

Cathode side transport phenomena investigation and Multi-Objective optimization of a tapered parallel flow field PEMFC

A Proton Exchange Membrane Fuel Cell (PEMFC) provides stable, emission-free, high-efficiency power. Water management and durability of PEMFCs are directly affected by transport phenomena at the cathode side. In the present study, transport phenomena are investigated and optimized in a tapered parallel flow field. Main channels in the flow field are tapered, which increases limiting current density by 41%. Two objectives, i.e. water saturation and transport resistance, are considered metrics for transport phenomena in a tapered parallel flow field PEMFC. Operating pressure, temperature, stoichiometries at both sides, and the porosity of gas diffusion layers are selected as parameters to be optimized. Two functions are generated for objectives by integrating 3D multiphase-flow computational fluid dynamics and Response Surface Methodology. Multi-Objective Optimization (MOO) is carried out with two different methods. Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to produce two challenging Pareto fronts. The results demonstrate that MOPSO performs better than NSGA-II. MOPSO recognized quite the same Pareto front with lower runtime. In the last step, the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is used to select an optimum point from the Pareto front. The results are compared against experimental data, and good correspondence is observed. The optimum features are temperature 323, pressure 1 atm, anode stoichiometry 3, cathode stoichiometry 2.62, and porosity 0.68. The porosity and pressure played the most significant roles in determining water saturation and resistance.

SEISMIC-DERIVED ROCK TRUE RESISTIVITY (Rt ) REVISITED. PART II: REFORMULATION USING WYLLIE’S TIME-AVERAGE MODEL

This paper is the second of a three-part presentation. As highlighted in the previous paper (Part I, Widarsono Mendrofa, 2006), the main objective of the study is to re-evaluate the potential of acoustic impedance as a source of resistivity data. This essentially came from the very idea of extracting information of resistivity (Rt ), data that plays a very important role in the determination of water saturation in reservoir, from seismic-derived acoustic impedance (AI).As observed in the past view years, there have been a lot of efforts devoted to the extraction of water saturation information from seismic. However, as Widarsono Mendrofa (2006) put it, most of the efforts were mainly based on pattern recognition activities with little attention was given to the theoretical aspects of relationships between seismic signals and water saturation. The work reported in this threepart presentation is concentrated more as re-establishing (a reformulation of works reported in Widarsono Saptono, 2003; 2004) the theoretical relationship between resistivity and acoustic impedance.In the Part I (Widarsono Mendrofa, 2006), a reformulation between the classical Gassmann acoustic velocity model and shally sand models of Modified Simandoux and Hossin is presented. In the reformulation, a new resistivity function of acoustic impedance has been established. In principle, whenever acoustic impedance data from seismic has been made available resistivity data for the determination of fluid saturation can be estimated.Despite the theoretical correctness of the resistivity function presented in the Part I, practicallity is not the function’s best aspect. In other words, the resistivity function is not an easy one to be used practically. Various parameters (e.g. matrix moduli) have to be assumed, since the data cannot easily measured even in the laboratory. This is indeed the main reason why gassmann model, and others such as Biot, has not been used much in day-to-day practices such as log interpretation for porosity determination.Being aware of such difficulties, in 1954 M.R.J. Wyllie et al proposed their “time average” model (named after its proportional averaging of pore fluid, rock matrix, and shale transit time values to represent transit time of a fluid-filled porous medium) for any practical uses related to P-wave velocity in porous media. Due to its simplicity, the model, as well as its subsequent modifications, has been used extensively since then in some areas especially in log analysis for porosity determination. Considering this simplicity aspect, this three-part study also adopted Wyllie “time average” model into its reformulation works. This Part II paper presents the formulation using Wyllie and the two shally sand models following the same manner that was adopted and presented in the Part I paper.Summarily, the objectives of the works presented in this paper are:- To establish a model/method to obtain formation rock true resistivity (Rt ) from seismic-derived acoustic impedance (AI),- To provide correction/modification onto previous works reported in Widarsono Saptono (2003, 2004), and- To provide a simpler alternative to the resistivity function yielded from the reformulation works presented in Part I paper (Widarsono Mendrofa, 2006)

The Importance Of Litho-Facies Distinction In Determining The Most Representative Cementation Factors For Well-Log Evaluation : An Old Issue Persistently Neglected

Cementation factor is a parameter always required in any conventional open-hole log analysis leading to determination of water saturation. Considering the central of water saturation in the estimation of hydrocarbon in place and reserves, any error in the use of the parameter may prove fatal. A common practice in the oil industry is that acquisition of laboratory-derived cementation factor has never been given a proper attention. It occurs very often that too few samples – hardly represent the rocks of reservoir of concern – are assigned for laboratory test. The practical use of the parameter in the log analysis also often draw question, in which un-representative cementation factor is arbitrarily used due to lack of the data. The effect of this practice has long been known but is often neglected – with all of its consequences – up to present day. This study tries to revive the awareness through presenting a fact that cementation factor may vary due to differences in litho-facies characteristics. Formation resistivity factor data from forty-seven limestone coreplug samples were taken from a West Java (WJ) field. Visual description over the samples has shown that they belong to several litho-facies types. Results of the study have mainly proved that different litho-facies type may have significantly different cementation factors. An averaging effect is also obvious when data from all samples are processed collectively. The effect of improper use of cementation factor is shown through the application of three water saturation models through which erroneous water saturation estimates are produced. The finding of the study is again hoped to reinforce the awareness of the use of proper and representative cementation factor

Study and Choice of Water Saturation Test Method for Tight Sandstone Gas Reservoirs

This study aims at investigating the applicability of the commonly used water saturation test methods such as distillation extraction method, nuclear magnetic resonance (NMR) method, constant weight method, and Coulomb method in the water saturation test of tight sandstone gas reservoirs. The above methods were studied and optimized to overcome the difficulties of low water content determination caused by low porosity, low permeability, small pore throat, high capillary force, and high bound water saturation of tight sandstone, to obtain relatively accurate water saturation data. The results showed that the water content data determined by the constant weight method were consistent with the known water content saturation, and the average error was 3.6%. By shortening the echo interval, the average error of water content saturation tested by the NMR method for tight sandstone samples was 4.8%. The two methods of the distillation method and Coulomb method had large errors, and both failed to meet the requirements of water saturation determination of tight sandstone, of which the average error of the former was 27.9% and the average error of the latter was 50.7%. Therefore, for tight sandstone, the constant weight method and NMR method were recommended to be used for water content saturation testing, and the distillation extraction method and Coulomb method were not recommended. The research results provided a new method for the accurate determination of water content saturation in tight sandstone gas reservoirs.

Comparison of Water Saturation Models Based on Well Logging Data: A Case Study of MX Field in Malay Basin

One of the primary methods to determine water saturation is logging measurement which uses water saturation models. Archie`s interpretation model to estimate water saturation in clean formations has successfully been useful over the years. However, in shaly sand formation this model yields inaccurate water saturation estimates due to shaly sand effects. Many shaly sand interpretation models have been developed; unfortunately; there is no unique water saturation model which works best for all formations as subsurface information may be limited and formation shaly sands may vary. Nevertheless, some water saturation models provide more accurate water saturation values for a formation. Thus, this study aimed to determine the water saturation alternative by using Dual-Water, Waxman-Smits, Indonesian, and Modified Simandoux models. The results showed that the Indonesian Model and Modified Simandoux gave the closest water saturation values with more than 80% similarities compared with logging analysis and thus should be considered the best models. Therefore, it is hoped that the findings can help improve knowledge on the selection of water saturation models best suited to the nature of formation similar to the wells, especially to other hydrocarbon exploration in the Malay Basin.

Artificial Neural Network Modeling for Water Saturation Determination in Shaly Sandstone Reservoirs: Case Study Egyptian Oil Field

Artificial Neural Network Modeling for Water Saturation Determination in Shaly Sandstone Reservoirs: Case Study Egyptian Oil Field

Modified Archie\u2019s parameters for estimating water saturation for carbonate reservoir in north of Iraq

Archie’s parameters, cementation factor (m), saturation exponent (n) and tortuosity factor (a), are general factor that have effects on water saturation magnitude, due to their sensitivity to pores distribution, lithofacies properties and wettability, particularly in carbonate reservoirs. Water saturation magnitude has a direct effect in estimating initial oil-in-place values, and inaccuracy in its values will lead to huge impact errors in initial oil-in-place values, so it would affect the economics of field management and development plans. In this paper, the main objective was to investigate the impact of using conventional and modified Archie’s parameters in the determination of water saturation from well log interpretation for Tertiary reservoir in Khabaz oil field, a heterogenous carbonite reservoir in the north of Iraq which was affected by different digenesis processes that impacted the reservoir quality. Tertiary reservoir of Khabaz field consists of five geological units (A, B, C, D and E), and the selected well penetrated the top of the reservoir at 2200.5 m RTKB and passed through five geological units and reached total depth at 2348 m RTKB. The geothermal gradient of the field was 1.12 ℉ per 100 ft, and formation water resistivity (Rw) was about 0.029 Ω m. Water saturation was at first estimated from resistivity logs by Archie model with conventional known values of parameters (a, m and n) (1, 2 and 2), respectively, and then Archie’s parameters were modified and determined by graphical technique of Pickett plot for each geological unit to estimate water saturation. Finally, the results show the water saturation value was more sensitive for Archie’s parameter in low-porosity and high-clay-volume zone, but less sensitive in clean high-porosity zone, and water saturation values determined by modified Archie model were less about 18.5% at mean than their value by using conventional Archie’s parameters.

Lithology Identification, Shale Volume Estimation and Formation Wate Saturation of Fenchuganj Gas Field Using Wireline Log Data

This study has been undertaken with a view to interpreting and analyzing different types of logs, detecting hydrocarbon-bearing sand zone, identifying the lithology, estimating shale volume, and determining water saturation of the formation of the Fenchuganj gas field. Fenchuganj gas field is in the southern part of Bangladesh in the Sylhet division. It lies in the Surma basin and characterized as a water drive gas field. Gamma Ray (GR) log and Spontaneous Potential (SP) log analysis help to interpret the formation lithology. After interpreting lithology, the gamma-ray log and SP log were analyzed further, and the hydrocarbon-bearing zone was detected. From the analysis of different logs, for well 4, so many gas-bearing sand zones have been detected. The gas-bearing zones are found between (1585-1595) m, (1600-1610) m, (2409-2420) m, (2265-2270) m, (2295-2303) m, (2435-2450) m, (2465-2480) m, (3150-3155) m, (2955- 2960) m. These are the gas-bearing zones of fenchugonj well-4 determined from the composite log of this field. Water-bearing sand zones are between (1708-1714) m and the fully compacted shale zone is between (1920-1930) m. From the analysis of different logs, for well 5, so many gas-bearing sand zones have been detected. The gas-bearing zones are found between (2365-2385.5) m, (1820-1830) m, (1782-1803) m, (305-308) m, (336.5-338.5) m, (607.5-609.5) m. These are the gas-bearing zone of fenchugonj well-5 determined from the composite log of the field. Shale volume has been estimated from the gamma-ray log and from resistivity logs by the true resistivity method. In well 4, the calculated value of shale volume by Gamma Ray method and true Resistivity methods were respectively 15% and 6%, in well 5 these values were 7.25% and 7.64%. Formation water resistivity was determined from the formula and taken as 0. 1ohm-m and mud filtrate resistivity 0.76 ohm-m. for well-4 and for well-5 it was taken as 0.7 ohm-m (from the provided composite log of well 4 and 5). Formation true resistivity Rt was estimated directly from the ILD log and true resistivity log. The average value of Rt for well-5 was 33.75 ohm-m and for well-4 the value is 34.09 ohm-m. From the RFOC log, flushed zone resistivity Rxo was calculated directly. Water saturation has been determined by three different techniques named Archie, Indonesia, and Simandoux method. For well 5, the value of average water saturation found from Archie, Indonesia, and Simandoux model was 8.5%,22%, and 24.84%, and for well-4. The value of average water saturation was found as 7.3%,18%, 20.51%respectively.

Using clay microporosity to improve formation evaluation in potential residual oil zones: Cypress Sandstone, Illinois Basin

Clay microporosity in sandstones can cause erroneously high water saturation calculations if not properly accounted for and reduces effective pore space when evaluating a formation. We conducted a petrographic study to characterize and quantify the clay mineral microporosity of the fluvial facies of the Mississippian Cypress Sandstone in the Illinois Basin. This data helped achieve more accurate saturation calculations through core and well log analyses in potential Cypress residual oil zones. Petrographic thin sections were analyzed by scanning electron microscopy with backscatter imaging and energy dispersive X-ray spectroscopy. Clay mineral species identified included pore-filling kaolinite books, blocks, and vermicules; chlorite clusters; illite mats; illite-smectite webs; grain-coating inherited chlorite rims; and pore-bridging illite hairs. Volume percentages of microporosity particular to clay mineral species were determined by image analysis. Average microporosity values in kaolinite, chlorite, illite, and illite-smectite were 41%, 57%, 67%, and 65%, respectively. Effective clay mineral volumes, including microporosity, showed a >2-fold increase over estimates determined by X-ray diffraction, constituting an average volume of 4%. Comparing these volumes with gamma-ray log estimates proved Stieber’s work to predict clay volume most accurately. Clay volumes were used as input parameters in the dual-water method for determining water saturation, resulting in greater residual oil saturations than those predicted from Archie’s equation. Accounting for clay microporosity in helium porosimetry measurements of total porosity resulted in an ~11% decrease to effective porosity. Results from this research improve estimates of water saturation and porosity and highlight the importance of studying microscale mineralogical properties during reservoir characterization.