Estimation Of Water Saturation Research Articles

Quantifying the Impact of Siderite Composition and Reservoir Resistivity (Rt) on Water Saturation Estimation in Low-Resistivity Sideritic Sandstone Reservoirs Using the Graphic Plot Method

Quantifying the Impact of Siderite Composition and Reservoir Resistivity (Rt) on Water Saturation Estimation in Low-Resistivity Sideritic Sandstone Reservoirs Using the Graphic Plot Method

Estimation of residual water saturation in 3D geological modeling

Estimation of residual water saturation in 3D geological modeling

Estimation of water saturation based on optimized models in tight gas sandstone reservoirs: a case study of Triassic Xujiahe Formation in northwestern Sichuan Basin

Estimation of water saturation based on optimized models in tight gas sandstone reservoirs: a case study of Triassic Xujiahe Formation in northwestern Sichuan Basin

Integrating Rock Typing and Petrophysical Evaluations to Enhance Reservoir Characterization for Mishrif Formation in Garraf Oil Field

Accurate reservoir characterization is essential for successful hydrocarbon extraction, especially in complex fields such as the Garraf Oil Field. This study aims to enhance reservoir characterization by integrating different petrophysical assessments and rock typing methodologies. Density, neutron, and sonic porosity evaluations were used to assess porosity, while gamma-ray logs and resistivity measurements were used to determine shale volume. The Archie equation was employed to estimate water saturation and sensitivity analysis was used to determine the cutoff values. The study also utilized rock typing techniques, including hydraulic flow unit assessment and Rock fabric number cross-plots, to categorize reservoir rocks into flow units and identify unique rock types. The combination of these approaches led to the precise identification of reservoir heterogeneities and optimal oil production zones. The results showed that the Gamma-ray log is the best method for determining shale volume, and the closest method for porosity determination is the density log. The water resistivity value was estimated at 0.016, while the Archie parameters (a,m,n) were 1.1, 2.1, and 3.7, respectively, with cutoff values of 0.22 for shale volume, 0.11 for porosity, and 0.56 for water saturation. The study identified five rock types ranging from packstone, pack to wackstone, wackstone, wack to mudstone, and mudstone. Overall, the integration of petrophysical evaluations and rock typing techniques facilitates the accurate delineation of oil-rich zones with enhanced reservoir connectivity.

Determination of Shale Types and their Volumes and Petrophysical Exponents for more Accurate Water Saturation Estimation for Raha Formation, Gulf of Suez, Egypt

Determination of Shale Types and their Volumes and Petrophysical Exponents for more Accurate Water Saturation Estimation for Raha Formation, Gulf of Suez, Egypt

Core Analysis Using Nuclear Magnetic Resonance

Core analysis is critical for oil and gas exploration and recovery. Nuclear magnetic resonance (NMR) has been widely applied to the petroleum industry for many years, and NMR core analysis plays an important role in supporting borehole NMR formation evaluation and understanding the mechanisms of petrophysics and petrochemistry. It’s unfortunate, however, that there has been a lack of commented descriptions of the basic procedures for NMR rock core analysis in the literature, which has led to issues of comparability and repeatability of NMR measurements between different laboratories. In this paper, we present a brief guideline for NMR rock core analysis of conventional T2, T1, T1-T2, and D-T2 measurements. As an established procedure in daily laboratory work, NMR core analysis includes three sophisticated steps: rock sample preparation, an NMR experiment, and data analysis. The acquisition and saturation of rock samples are described, and corresponding solutions are also considered. Detailed NMR experimental measurements, data setting, and calibration are addressed. Laplace inversion for different NMR experiments and models for estimation of porosity, irreducible water, permeability, pore structure, and oil saturation are included. The aim of this paper is to try to standardize general NMR experiments for rock core analysis.

Assessment of Depth of Mud-Filtrate Invasion and Water Saturation Using Formation-Tester Measurements: Application to Deeply Invaded Tight-Gas Sandstones

Formation pressure/fluid measurements are impacted by mud-filtrate invasion, which may require long fluid pumpout durations to acquire hydrocarbon samples with minimal mud-filtrate contamination. However, unlike other well-logging instruments, formation testers do not have a fixed depth of investigation that limits their ability to pump out mud filtrate until acquiring original formation fluids (i.e., sensing the uninvaded zone). We use an in-house petrophysical and fluid-flow simulator to perform numerical simulations of mud-filtrate invasion, well logs, and formation-tester measurements to estimate the radial distance of invasion and the corresponding radial profile of water saturation. Numerical simulations are initialized with the construction of a multilayer petrophysical model. Initial guesses of volumetric concentration of shale, porosity, water saturation, irreducible water saturation, and residual hydrocarbon saturation are obtained from conventional petrophysical interpretation. Fluid-flow-dependent petrophysical properties (permeability, capillary pressure, and relative permeability), mud properties, rock mineral composition, and in-situ fluid properties are obtained from laboratory measurements. The process of mud-filtrate invasion and the corresponding resistivity and nuclear logs are numerically simulated to iteratively match the available well logs and estimate layer-by-layer formation water saturation. Next, using our multiphase formation testing simulator, we numerically simulate actual fluid sampling operations performed with a dual-packer formation tester. Finally, we estimate irreducible water saturation by minimizing the difference between the hydrocarbon breakthrough time numerically simulated and measured with formation-tester measurements. The examined sandstone reservoir is characterized by low porosity (up to 0.14), low-to-medium permeability (up to 40 md), and high residual gas saturation (between 0.4 and 0.5). The deep mud-filtrate invasion resulted from extended overbalanced exposure to high-salinity water-based mud (17 days of invasion and 1,800 psi overbalance pressure) coupled with the low mud-filtrate storage capacity of tight sandstones. Therefore, the uninvaded formation is located far beyond the depth of investigation of resistivity tools, whereby deep-sensing resistivities are lower than those of uninvaded formation resistivity. Through the numerical simulation of mud-filtrate invasion, well logs, and formation-tester measurements, we estimated radial and vertical distributions of water saturation around the borehole. Likewise, we quantified the hydrocarbon breakthrough time, which matched field measurements of 6.5 hours. The estimated radius of invasion was approximately 2.5 m, while the difference between estimated water saturation in the uninvaded zone and water saturation estimated from the deep-sensing resistivity log was approximately 0.13, therefore improving the estimation of the original gas in place.

Developing a Saturation Height Function (SHF) for the classified Hydraulic Flow Unit, a case study from the Middle Miocene Carbonate reservoir in the Song Hong Basin, Vietnam

The Middle Miocene carbonate is the main gas bearing reservoir in the southern part of the Song Hong basin. Carbonate reservoirs present challenges for engineers and geologists to characterize because of their complexity due to depositional and diagenetic processes. The log based calculated water saturation Sw for this complex carbonate reservoir bears a lot of uncertainties due to its heterogeneity that strongly affects to m and n values in the water saturation equation. As the calculated Sw strongly affects the Hydrocarbon Initially In Place (HIIP) estimation, the production performance prediction, etc., the alternative method using the Saturation Height Function (SHF) based on the capillary pressure was introduced. The SHF building process uses the combination of the result of HFU classification and permeability prediction given by machine learning methods, which have become a very useful tool for complex reservoir characterization such as carbonate reservoir for decades. Our obtained results allowed us to subdivide the carbonate reservoir into 5 Hydraulic Flow Unit (HFU) by using unsupervised machine learning methods, and then the capillary pressure (Pc) curves were classified and a proper saturation-height function using Skelt Harrison equation was assigned to individual HFU after testing with common SHF equations. The HFU number for each individual point was predicted based on the log data measured along the borehole using the supervised machine learning techniques while the Sw was calculated using the chosen SHF model and the FWL defined on log curves and pressure data from RCI measurement. The Sw computed from the SHF model is reliable and can be used for water saturation estimation for the whole field for known FWL and predicted HFU numbers.

Matrix decomposition methods for accurate water saturation prediction in Canadian oil-sands by LF-NMR T2 measurements

This article presents a novel method for quantifying water saturation in oil-sand reservoirs by employing 1D low-field nuclear magnetic resonance (LF-NMR) spin-spin relaxation and bulk density measurements as indicators of pore volume variations. One of the challenges in accurately determining the volumes of bitumen and water in oil-sands is the effective separation of their overlapping T2 signals, attributed to similar spin-spin relaxation decay times and diffusive coupling. Conventional methods require deconvolution of T2 peaks and or experimentation to determine T2 cutoff values, differentiating between bitumen and water signals, notably capillary and clay-bound water. In contrast, our approach predicts the proportion of water by utilizing matrix decomposition methods to compress the T2 relaxation distribution and extract significant components. These components subsequently train the regression model, facilitating the accurate estimation of relative water saturation percentages.The NMR dataset was obtained by benchtop LF-NMR T2 measurements from 82 oil-sand samples, with preserved bitumen and water saturations at both reservoir and ambient temperatures (6 °C and 25 °C), yielding 164 observations. We examined four matrix decomposition methods, including principal component analysis, its variation integrating a kernel function, canonical correlation analysis, and partial least squares regression. X-ray CT measurements and Dean-Stark extraction ascertained the respective sample bulk densities and fluid-solid volume proportions.The PCA model prediction statistics (RMSE = 0.86%, R2 = 0.84), indicate its application can be extended for saturation prediction from NMR and bulk density well logs. Moreover, we underscore the importance of incorporating bulk density measurements and establish the statistical and physical correlations between these measurements and NMR T2 relaxation, providing insights into the approach's efficacy and causality.

Petrophysical interpretation of logging-while-drilling borehole measurements in the presence of electrical anisotropy, mud-filtrate invasion, noise, and well deviation effects

Petrophysical interpretation of logging-while-drilling (LWD) borehole measurements in the presence of electrical anisotropy, mud-filtrate invasion, noise, and well deviation effects remains a challenge in formation evaluation. In high-angle and horizontal (HAHZ) wells penetrating electrically anisotropic sandstones, phase and attenuation apparent resistivity logs often exhibit values larger than resistivity parallel to bedding planes. Consequently, traditional interpretation methods often fail to accurately estimate hydrocarbon saturation when using only the long-spacing phase apparent resistivity log (P40H) in petrophysical calculations. We implement two approaches (analytical and Bayesian) to estimate horizontal and vertical resistivities from apparent resistivity measurements. The first analytical approach is based on the numerical simulation of LWD resistivity measurements using synthetic models under various conditions of electrical anisotropy and well inclination to derive analytical models. The relative error of analytical approximations is less than 6%. In the presence of measurement noise and shoulder-bed effects, we implement a Bayesian method for well-log inversion and uncertainty quantification. The relative error of Bayesian inversion is less than 2%. Estimates of horizontal and vertical resistivities then can be used for petrophysical analysis. A challenging set of synthetic and field examples of HAHZ wells penetrating electrically anisotropic formations is selected for examination and verification of the new methods. For radial lengths of invasion less than 20 cm (8 in), sensitivity analysis indicates that the effects of conductive mud-filtrate invasion on long-spacing resistivity logs are negligible. The long-spacing phase (P40H) and attenuation (A40H) apparent resistivities are used to estimate horizontal and vertical resistivities. In the field examples under study, electrical anisotropy is caused by intercalated laminations of coarse- and fine-grained sandstones; the volumetric concentration of shale is negligible. Considering that Archie’s parameters are direction dependent, sensitivity analysis indicates that the effective value of the saturation exponent parallel to bedding planes is less sensitive to the volumetric concentration of fine-grained sandstone layers compared with the saturation exponent perpendicular to bedding planes. Thus, water saturation of grain-laminated sandstones in HAHZ wells can be estimated using horizontal resistivity with Archie’s parameters that are consistent with petrophysical interpretations performed in vertical wells. Estimates of water saturation in the field examples using horizontal resistivity agree with saturation-height models. Compared with conventional interpretation methods that use P40H as the formation resistivity, the new approach improved the estimation of hydrocarbon saturation by 10%.

A theoretical approach for water saturation estimation in shaly sandstones

The process of quantitative reservoir characterization is a crucial component spanning from the initial estimation of in-place reserves to the optimization of production design. Water saturation is a critical parameter in quantitative reservoir characterization that directly impacts the in-place reserve estimates. In the case of shale-free sandstones, the computation of water saturation using Archie's equation yields fairly accurate results. However, in shaly sandstone reservoirs, the presence of multiple conductive entities in the rock poses additional complexities. Currently, various empirical, semi-empirical and theoretical equations are available in literature to estimate water saturation; nevertheless, none of these models is such that it is both theoretically sound and practically applicable.In this study, we present a novel approach that is both theoretically sound and practically applicable in computing water saturation in shaly sandstones. The fluid-saturated rocks are treated as a system consisting of two interlocked lattice-like structures, namely grains and fluid in the interconnected pores. The composite fluid comprising water and hydrocarbon is modeled using the general mixture rule, while the Hanai-Bruggeman equation is employed to model the electrical conductivity of this system. The resulting electrical conductivity equation is then utilized to compute water saturation in shaly sandstones.The proposed equation was effectively validated using the published core data. Furthermore, we compare the new model with other grain conductivity-based models such as Mixing model and Berg's model. Our findings show that while describing core data the proposed equation gives a lower average Relative Root Mean Squared Error (RRMSE) of 2.05% as compared to 7.56% and 5.58% for the Mixing and Berg's model available in literature, respectively. Moreover, the range of porosity and water conductivity for which the proposed model is applicable, surpasses that of the current models. We also suggest that this equation can be extended to other systems with a similar two interlocked lattice-like structure, such as carbonates.

Water contact mapping and saturation estimation using simultaneous inversion of time-lapse seismic data constrained by fluid substitution and Leverett J-function analysis

The integration of a Leverett J-function analysis with the Gassmann and mass-balance equations allows the time-lapse change of seismic reflectivity to be expressed explicitly in terms of the change of the free water-contact (WC) level, reservoir porosity, and initial water saturation. Given the initial 3D distribution of water saturation and porosity and a well-log derived height-saturation-porosity function as a hard inversion constraint, synthetic modeling suggests that time-lapse seismic data can potentially be directly inverted for the change in free-water level, and time-lapse changes in the spatial variation of water saturation can thus be predicted. This integrated inversion approach is applied to a real time-lapse seismic difference volume from the Maui-B field in the Taranaki Basin, New Zealand, where the change of WC level from a baseline model is the only free parameter to be inverted and is obtained by searching for the global error minimum between tmodeled and real time-lapse seismic differences as the modeled WC level is varied. The method correctly predicts the gas depletion and a measurable rise of the water contact on the southern flank of the field.

Estimation of the liquid water saturation in PEM fuel cells: A low-power peaking-free dead-zone observer approach

Water management in polymer electrolyte membrane fuel cells (PEMFCs) is one of the most challenging issues affecting PEMFC efficiency and lifetime. The unavailability of reliably liquid water saturation sensors hinders the applicability of liquid water active control and supervision techniques. A promising technique that can be applied in this context are high-gain observers. However, the performance of this type of observer is significantly limited by the peaking phenomena and its noise sensibility. In general, this performance is not adequate for the considered estimation problem. For this reason, this work proposes a new high-gain observer without peaking and with reduced noise sensibility. The convergence of the observer is proven through rigorous arguments. Moreover, the algorithm is shown to be applicable in PEMFC systems through numerical simulations and experimental validation. It is shown that the proposed approach achieves a reduction of 32.3% of the mean square error in the estimation while maintaining the convergence rate and robustness of classical high-gain observers.

Automatic Multiwell Assessment of Flow-Related Petrophysical Properties of Tight-Gas Sandstones Based on the Physics of Mud-Filtrate Invasion

Summary Petrophysical interpretation of borehole geophysical measurements in the presence of deep mud-filtrate invasion remains a challenge in formation evaluation. Traditional interpretation methods often assume a piston-like radial resistivity model to estimate the radial length of invasion, resistivities in the flushed and virgin zones, and the corresponding fluid saturations from apparent resistivity logs. Such assumptions often introduce notable inaccuracies, especially when the radial distribution of formation resistivity exhibits a deep and smooth radial front. Numerical simulation of mud-filtrate invasion and well logs combined with inversion methods can improve the estimation accuracy of petrophysical properties from borehole geophysical measurements affected by the presence of mud-filtrate invasion. We develop a new method to quantify water saturation in the virgin zone, residual hydrocarbon saturation, and permeability from borehole geophysical measurements. This method combines the numerical simulation of well logs with the physics of mud-filtrate invasion to quantify the effect of petrophysical properties and drilling parameters on nuclear and resistivity logs. Our approach explicitly considers the different volumes of investigation associated with the borehole geophysical measurements included in the interpretation. The new method is successfully applied to a tight-gas sandstone formation invaded with water-base mud (WBM). Petrophysical properties were estimated in three closely spaced vertical wells that exhibited different invasion conditions (i.e., different times of invasion and different overbalance pressures). Available rock-core laboratory measurements were used to calibrate the petrophysical models and obtain realistic spatial distributions of petrophysical properties around the borehole. This approach assumes that initial water saturation is equal to irreducible water saturation. Based on the calibrated petrophysical models, thousands of invasion conditions were numerically simulated for a wide range of petrophysical properties, including porosity and permeability. Based on the large data set of numerical simulations, analytical and machine-learning (ML) models were combined to infer unknown rock properties in each well. Mean-absolute-percent errors (MAPE) of the analytical and ML models for the estimation of water saturation in the virgin zone are 5% and 2%, respectively, while the MAPE of the analytical models for the estimation of residual hydrocarbon saturation is 10%. Synthetic and field examples are examined to benchmark the successful application and verification of the new interpretation method. Estimates of water saturation in the virgin zone using the new method are in good agreement with core-based models.

A New Workflow for Improved Resistivity-Based Water Saturation Assessment in Organic-Rich Mudrocks: Application to Haynesville, Eagle Ford, and Woodford Formations

Summary Reliable fluid saturation assessment in organic-rich mudrocks has been a challenge for the oil and gas industry. The composition and spatial distribution of rock components have a significant impact on electrical resistivity and, thus, on hydrocarbon reserves estimates. Clays are typically considered, in resistivity models, to be distributed in laminated or dispersed forms. Additionally, conventional resistivity models do not incorporate conductive components other than brine. Such assumptions can lead to uncertainty in fluid saturation assessment in organic-rich mudrocks. We introduce a well-log-based workflow that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks and demonstrate its field application in the Eagle Ford, the Woodford, and the Haynesville formations. The introduced workflow consists of an inversion algorithm to estimate geometry-dependent parameters (depolarization factors or geometric model parameters) and water saturation. Inputs to the inversion algorithms include volume concentrations of minerals, estimated from the multimineral analysis. Other inputs are conductivity of rock components and porosity obtained from laboratory experiments and interpretation of well logs. The petrophysical model considers that brine forms the conductive background to which conductive (e.g., clay, pyrite, and kerogen) and nonconductive (e.g., grains and hydrocarbon) components are incorporated. The assumed/estimated petrophysical properties have an impact on the effective conductivity of the rock and thereby can impact the performance of the new resistivity-based method. We applied the new method to different organic-rich mudrock formations to test the universal nature of the method and its efficacy in organic-rich mudrock reservoirs with varying volumetric concentrations of minerals within the rock. We successfully applied the workflow to four wells in the Eagle Ford, the Woodford, and the Haynesville formations. The formation-by-formation inversion showed a variation in geometric model parameters in different petrophysical zones, resulting in improved water saturation estimates. A comparison of the results obtained from the new workflow against those from the Waxman-Smits and Archie models indicated a relative improvement in saturation estimates of 9.5 and 26.3% in the Eagle Ford formation. Similar improvements were noted in the Woodford and the Haynesville formations as well. The improvement can be enhanced in formations with larger fractions of conductive components. The results confirmed that the new workflow improves the reliability of water saturation estimates in organic-rich mudrocks, which has been a challenge for the oil and gas industry. In contrast to conventional techniques, the new method does not need water saturation obtained from core measurements for calibration efforts. All the parameters in the new workflow are geometry- or physics-based. We verified that formation-based geometric model parameters in the Eagle Ford formation were consistent in both wells, which is promising for calibration-free assessment of water/hydrocarbon saturation in the field-scale domain using electrical resistivity measurements. The new method minimizes the need for expensive and time-consuming core measurements of water saturation, which is a unique contribution of this work. Finally, the new workflow is physics-based and incorporates the volumetric concentration and electrical conductivity of all rock components. This enables the introduced workflow to be applied to different formations with ease for improved assessment of water saturation.

Amplitude variation with angle and azimuth inversion to estimate fracture properties in shale-gas reservoirs

Seismic amplitude variation with angle and azimuth inversion is an effective tool for estimating subsurface anisotropic properties from seismic data. Generally, existing methods are oriented toward obtaining elasticity constants, anisotropic coefficients or fracture weaknesses, which are not direct indicators for the exploration and development of hydrocarbons, unlike fracture parameters. Moreover, the conversion from elastic to fracture parameters is based on models subject to error propagation. To overcome these problems, the fracture weaknesses are formulated as a weighted representation of fracture density and aspect ratio and fluid properties. Then, an inversion method is proposed that incorporates the fracture aspect ratio as a spatial variable, which can be applied to estimate water saturation and permeability. Compared with the conventional methods, our approach yields more geological information, as shown by an application to real data.

Cementation factor in clayey rock samples: investigating the role of clay content and determination using electrical rock classification

Accurate determination of cementation factor (m) has a significant role in the petrophysical characterization of underground reservoirs. For clean sandstones, the application of this parameter in Archie's Equation provides a basic framework for estimating water saturation. However, the presence of clay minerals in the rock texture could affect the evaluation process. There are different correlations from which the cementation factor can be estimated. In addition, applying rock classification techniques revealed an excellent prospect for better estimating this parameter. However, in most of the mentioned approaches, the effect of clay minerals is missed. In this study, a newly developed modified form of electrical rock typing technique (i.e. classification of rock samples according to their electrical properties) incorporating the effect of clay minerals for improving the prediction of cementation factor in clay-bearing rocks is proposed. In doing so, a new form of electrical quality index is defined based on which the modified formation resistivity factor versus porosity data for 204 clayey rock samples are classified into nine distinct rock types. Comparative studies demonstrated the priority of the suggested methodology rather than classic hydraulic flow unit-based methods such as flow zone indicator and discrete rock type. The latter showed a high degree of scattering in the classification stage. For each rock type, developed based on the proposed scheme, the equations were proposed in which the modified form of formation resistivity factor and cementation factor versus porosity were correlated. Linear relationships with reasonable values of coefficient of determination were then demonstrated for the developed equations. In addition, the effect of clay minerals on the rock's electrical quality and electrical properties (i.e., rock conductivity and cementation factor) is discussed. The results of this study offer a better framework to incorporate the effect of clay constituents in the prediction of cementation factor, as compared to classical rock typing approaches.

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

AbstractA Bayesian inversion methodology is proposed that inverts angle‐stacked 4D seismic maps to changes in pressure, water saturation and gas saturation. The inversion method is applied to data from a siliciclastic reservoir in the west of Shetlands, UK continental shelf. We present inversion results for three seismic monitor surveys and demonstrate the added value of pressure‐saturation inversion by providing insights into reservoir connectivity and fluid dynamics across 14 years of reservoir production. In these surveys, 4D seismic signals related to waterflood, pressure increase and depletion, and gas exsolution are evident and overlap each other in many regions. To regularize this ill‐posed inversion problem, we propose a Bayesian formulation that incorporates spatially variant prior information derived from a history‐matched reservoir simulation model and well pressure measurements. The benefit of incorporating these multi‐disciplinary data as prior information is demonstrated by comparing to inversion results using a spatially invariant prior. We show that the method takes advantage of the multi‐disciplinary prior information to make more precise inferences where the seismic data are most uncertain. This leads to more realistic spatial distributions for the pressure and water‐saturation inversion results. The non‐uniqueness in this non‐linear inversion is studied by analysing uncertainty estimates produced by stochastic sampling of the Bayesian posterior distribution. Posterior standard deviations are observed to be related to the sensitivity of the seismic amplitudes to the changes in each dynamic property as well as the degree of overlap between changes in different dynamic properties. Estimated pressure increases have a posterior standard deviation of approximately 1 MPa, whereas posterior standard deviations for pressure decrease are on average 4 MPa, with higher values applicable to regions of gas exsolution. The posterior standard deviation for estimates of gas saturation change is 0.07 for moderate, visible saturation signals, but 0.005 for low‐to‐zero gas saturation change. The posterior standard deviation for estimated water saturation change is mainly influenced by overlapping changes in pressure and gas saturation. When water changes dominate the 4D seismic signal, posterior standard deviations are on average 0.05. These values rise to 0.25 in areas where the water change is obscured by pressure or gas‐saturation changes.

Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 1

Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.

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.