Data For Reservoir Characterization Research Articles

Integration of FWI-derived reflectivity in seismic interpretation

Full-waveform inversion (FWI) processing provides an improved and higher-resolution velocity model. This study focuses on how to use FWI products in seismic interpretation. One such product is FWI-derived reflectivity (FDR), which often has better illumination than migrated seismic images. We want to go beyond structural interpretation and utilize FDR data in reservoir characterization (e.g., fault imaging, resolution, and amplitude fidelity). FWI can be performed up to the maximum frequency available in the input seismic data. However, in the case of our study area in offshore Trinidad, the FDR data set is based on acoustic FWI with frequency only up to 10 Hz. While comparing amplitude extractions from full-stack and FDR data, we observe complementary amplitude distribution. Similar complementary information is found when we decompose the data in frequency bands (higher-frequency migrated seismic data and lower-frequency FDR data). We discuss the integration of the FDR volume in seismic interpretation with data examples. We combine FDR data with full-stack seismic data in two ways to generate new attributes for reservoir mapping and to reduce vertical and lateral uncertainty.

Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Estimation of petrophysical properties holds significant importance in evaluating the feasibility of hydrocarbon extraction and enhancing production efficiency. Therefore, theintegration of seismic data with well data for reservoir characterization has garnered significant attention in the oil and gas industry. This approach provides a comprehensive view of the lateral variations across the entire reservoir. In this study, our goal is to establish a relationship between seismic attributes and the petrophysical properties of the reservoir, enabling the prediction of property distribution across the entire reservoir. We employed geostatistical inversion to generate detailed P-impedance maps of the reservoir, capturing its spatial heterogeneity. A total of 100 realizations were produced and ranked into P10, P50, and P90 scenarios, representing different probabilistic outcomes. The P90 scenario, considered the most representative of the reservoir's average properties, was selected for interpretation, enabling a comprehensive analysis of reservoir heterogeneities and their distribution. Subsequently, we estimated porosity using various seismic attributes, employing a Data-driven Extreme Gradient Boosting (Xgboost) approach within the reservoir analysis. This method enabled us to estimate a high-resolution acoustic impedance and porosity with minimal root mean square error of 3.8 and r2score of 66 % across the spatial gap which ranges from 8468.84 to 9367.85(g/cc) *(m/s) and 10 to 45 % between wells, revealing the lateral variation of acoustic impedance and porosity within the reservoir interval of interest. The information provided was found to be of significant value in the identification of potential sweet spots and plays a pivotal role in the development of a reservoir management strategy for future hydrocarbon exploration.

Reservoir oriented migrated seismic image improvement and poststack seismic inversion using well-seismic mistie

In seismic exploration of subsurface hydrocarbon reservoirs, migrated seismic image is the foundation for structural interpretation and subsequent seismic inversion for reservoir properties. Seismic events in migrated images can be mispositioned due to several factors including seismic velocity inaccuracy. The mispositioned events lead to poor well-seismic tie and degrades the accuracy of subsequent inversion and reservoir prediction. Particularly, the mistie can be quite obvious for the mid- and mature- phases of the reservoir development after many wells drilled. We propose to utilize the information from mistie between seismic images and synthetic traces to improve the accuracy of migrated seismic images and thus inversion results. Our method includes three major steps: firstly, calculate time shifts between the synthetic traces and the co-located seismic trace using the path-limited dynamic time warping (PDTW) algorithm; then compute the time shift volume for the entire migrated image using the seismic-driven modeling approach based on the time shift traces at the well locations; apply the time shift volume to update the migrated image. The improved seismic volume can be used for further processing such as seismic inversion, which makes the inversion results more consistent with seismic and well data. We apply our method to synthetic and field datasets. The results demonstrate that the updated seismic image has an improved seismic well tie and an improved inversion result. Our method indicates a venue for a better honor of seismic and well data in reservoir characterization.

Integration of Seismic and Well Log Data for Reservoir Characterization in Offshore Niger Delta, Nigeria

The demand for fossil fuels is continually increasing due to the growing global population and industrialization. It is thus necessary to find hydrocarbon prospects in new fields and those that have previously been classified as marginal fields in order to maximize production. An integrated approach is required to effectively characterize hydrocarbon reservoirs and assess their ability to store and produce hydrocarbons. 3D seismic reflection data and well log data were combined to quantitatively estimate hydrocarbon reserves in a field, offshore Niger delta, Nigeria. The reservoirs were delineated on the gamma ray and resistivity logs which penetrate four wells selected from the field. The petrophysical analysis provided information about the net-to-gross thickness ratio, porosity, shale volume and water saturation of the reservoirs, deduced from well log suites comprising gamma, resistivity, density and neutron-porosity logs through four wells. The seismic interpretation involved mapping of horizons and faults across the wells on the seismic section. Check shot data were used to tie the seismic data to the well log data to generate the synthetic seismogram. The time structural and depth structural maps were generated. The volumetric analysis entailed derivation of the Gross rock volume from the depth structural map, and estimation of the Stock Tank Oil Initially In Place (STOIIP). Four reservoirs were delineated in each of the four wells. The average thickness of the reservoirs ranges from 56.6 m to 232.7 m while the water saturation varies from 0.29% to 0.57%. The average porosities of the reservoirs ranges from 0.18% to 0.22%. The structural interpretation of the eight faults mapped reveals synthetic and antithetic faults, and rollover anticlines. The time- and depth structural maps generated from the mapped horizons show that the reservoirs are penetrated by the different faults. The Stock Tank Oil Initially In Place (STOIIP) of the reservoirs range from 25 MMstb to 468 MMstb. The study shows that the integration of 3D reflection data and well log data can be used effectively to estimate hydrocarbon reserves. The results of the study are expected to contribute to the development of new exploration and production strategies in the Niger delta basin and similar sedimentary environments.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

Building of a Static Model for Jeribe Formation in Jambour Oil Field

The Jeribe Formation, the Jambour oil field, is the major carbonate reservoir from the tertiary reservoirs of the Jambour field in northern Iraq, including faults. Engineers have difficulty organizing carbonate reserves since they are commonly tight and heterogeneous. This research presents a geological model of the Jeribe reservoir based on its facies and reservoir characterization data (Permeability, Porosity, Water Saturation, and Net to Gross). This research studied four wells. The geological model was constructed with the Petrel 2020.3 software. The structural maps were developed using a structural contour map of the top of the Jeribe Formation. A pillar grid model with horizons and layering was designed for each zone. Following the porosity model, the average porosity was roughly 11%. According to the permeability model, the reservoir has average permeability of 150 md while average net to gross, water saturation and thickness were 54%, 61%, and 75.5m, respectively. Depending on the average values of each unit separately zones two and three had good petrophysical characteristics, with about 85% of the Jeribe reservoir containing hydrocarbons.

Coupled Hydromechanical Modeling and Assessment of Induced Seismicity at FWU: Utilizing Time-Lapse VSP and Microseismic Data

The objective of this work is to utilize integrated geomechanics, field vertical seismic profile (VSP) and microseismic data to characterize the complex subsurface stress conditions at the Farnsworth Unit (FWU). The model is based on a five-spot sector model extracted from a primary geomechanical model. The five-spot well injection pattern is characterized by extensive reservoir characterization data, such well logs, extracted cores and borehole geophone data, to facilitate the detailed examination of stress changes and microseismic event occurrences. The study utilizes field vertical seismic volumes acquired from the injection well 13-10A. The seismic volumes successfully provided snapshots of the behavior of the reservoir at distinct times. The use of VSP and microseismic data provided direct and indirect estimates of the dynamic stress changes occurring in the overburden, reservoir and underburden rock formations. In order to illuminate the stress regions and identify rocks that have undergone inelastic failure, microseismic event occurrences were utilized. Microseismic activity has been detected at the FWU; further study of its locations, timing, and magnitude was needed to deduce the nature of the changing stress state. The results of the study revealed that microseismic events were successfully modeled within the Morrow B formation. Moment magnitudes of seismic events were within the same magnitudes for events in the reservoir, suggesting the suitability of the model. The results of the study showed that the computed moment magnitudes for seismic events were insignificant to warrant safety concerns. The study findings showed the usefulness of coupled hydromechanical models in predicting the subsurface stress changes associated with CO2 injection. The knowledge gained from this study will serve as a guideline for industries planning to undertake underground CO2 storage, and characterize the subsurface stress changes.

Rayleigh Wave Dispersion Spectrum Inversion Across Scales

Traditional approaches of using dispersion curves for S-wave velocity reconstruction have limitations, principally, the 1D-layered model assumption and the automatic/manual picking of dispersion curves. At the same time, conventional full-waveform inversion (FWI) can easily converge to a non-global minimum when applied directly to complicated surface waves. Alternatively, the recently introduced wave equation dispersion spectrum inversion method can avoid these limitations, by applying the adjoint state method on the dispersion spectra of the observed and predicted data and utilizing the local similarity objective function to depress cycle skipping. We apply the wave equation dispersion spectrum inversion to three real datasets of different scales: tens of meters scale active-source data for estimating shallow targets, tens of kilometers scale ambient noise data for reservoir characterization and a continental-scale seismic array data for imaging the crust and uppermost mantle. We use these three open datasets from exploration to crustal scale seismology to demonstrate the effectiveness of the inversion method. The dispersion spectrum inversion method adapts well to the different-scale data without any special tuning. The main benefits of the proposed method over traditional methods are that (1) it can handle lateral variations; (2) it avoids direct picking dispersion curves; (3) it utilizes both the fundamental and higher modes of Rayleigh waves, and (4) the inversion can be solved using gradient-based local optimizations. Compared to the conventional 1D inversion, the dispersion spectrum inversion requires more computational cost since it requires solving the 2D/3D elastic wave equation in each iteration. A good match between the observed and predicted dispersion spectra also leads to a reasonably good match between the observed and predicted waveforms, though the inversion does not aim to match the waveforms.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Petrophysical evaluation of well log data for reservoir characterization in Titas gas field, Bangladesh: a case study

The petrophysical evaluation of the Titas gas field (Titas-15) has been carried out for its reservoir characterization and to quantify the reservoir hydrocarbon prospects and gas productivity. The Quick Log Analysis Tool (QLAT) of PE2 Essentials software has been utilized for the computer-processed interpretation inputting the various well log data such as gamma-ray (GR), density log (RHOB), density-porosity (DPHI), neutron porosity (NDHI), resistivity logs, etc. Among the three reservoir sand groups, three gas-bearing sub-zones have been identified in the upper sand group, which are primarily producing gas from the least net-pay of 9.5 m, 39.5 m and 28.5 m, respectively. The shale volume is calculated based on GR, caliper logs and neutron-density (N-D) cross-plot (minimum shale volume) methods whose average value is comprised between 26.3 and 33.9%. The range of effective porosity is 18.4–18.6% indicating a good reservoir quality; permeability is 14.11–25.95 mD; recognizing as the well-sorted nature of sands. Additionally, the hydrocarbon saturation in the respective zones varies from 48% to 60%, suggesting a good hydrocarbon reservoir, whereas the water saturation is 40–52%. Moreover, the range of measured ‘gas initially in place’ in upper, middle and lower gas sands are 2371.6–2579.1, 10330.7–11206.1 and 6373.1–7110.5 MMscf, respectively, implying an adequate gas reserve on commercial criteria. From the evaluation of petrophysical properties, the Titas reservoir has a higher ability to enhance hydrocarbon accumulation and production.

Linkages between hydrothermal alteration, natural fractures, and permeability: Integration of borehole data for reservoir characterization at the Fallon FORGE EGS site, Nevada, USA

Between 2015 and 2018, the Fallon FORGE EGS (Engineered or Enhanced Geothermal System) site in Churchill County, Nevada, USA, underwent detailed geoscientific characterization to evaluate its suitability to operate as a field demonstration site to test and develop EGS technologies. This included the compilation, synthesis and acquisition of geophysical, geological, geochemical, hydrological, down-hole log, and mineralogy datasets. Site activities culminated in the drilling of a 2481 m-deep EGS observation well (21−31) to further verify the geology and rock properties of the proposed EGS reservoir formations, which are older than the Miocene and include felsic intrusions, felsic volcanics, and meta-sedimentary lithologies. In this paper, we integrate and evaluate borehole data from well 21−31, including drill cuttings and sidewall cores (petrographic, XRD, hyperspectral), wireline geophysical logs (e.g., gamma, neutron porosity, resistivity, sonic scanner), borehole imagers (FMI, BHTV), and pressure and temperature logs. Hydrothermal alteration and veining is found throughout the entire pre-Miocene interval of well 21−31, and includes epidote mineral assemblages that suggest past temperatures >250 °C. This is higher than the hottest measured equilibrated temperature at similar depths in other wells on the FORGE site today, and suggests that the system was higher temperature in the past. Overall lithologic variability is high, with volcanic, intrusive, metamorphic, and sedimentary rocks present in the bottom 700 m of the well. Natural fractures are abundant throughout the pre-Miocene section, but are more frequent in the felsic intrusive and felsic volcanic lithologies. The dominant orientation of these natural fractures observed in FMI logs is NNE-SSW with an average strike of 010° ± 40°, subparallel to the interpreted SHmax orientation of 009° ± 14° that is indicated by drilling-induced structures in the wellbore in the BHTV log. Therefore, natural fractures appear optimally oriented for reactivation via hydraulic stimulation. Mud loss locations and a non-equilibrated temperature log suggest that there are some zones with relatively-higher permeability and/or open fractures. However injection test results indicate an injectivity index of 0.025 L/min/kPa, which is an order of magnitude lower than a typical threshold for commercial wells. Analytical modeling of injection test results indicate that the majority of the well is low permeability (< 10−16 m²), with some discrete, localized zones of higher permeability (∼ 10 -12 m², less than 0.5 percent of the pre-Miocene (deep) section intersected by the well).

Optimizing geothermal production in fractured rock reservoirs under uncertainty

Geothermal energy resources are highly available but under-utilized throughout the world. Previous studies have sought to understand reservoir response to geothermal production and optimize production rates to maximize lifetime profits of geothermal reservoirs; however, these studies largely overlook the uncertainty in subsurface structure, such as the number of preferential flow pathways (e.g., fractures) that are available for fluid and heat transport. We present a combination of simple analytical models that maximize the profits of an idealized synthetic geothermal reservoir, while considering reservoir structural uncertainty, using expected net present value. For a typical case, we find that effective reservoir transmissivity and inter-well spacing exert the largest controls on the lifetime profitability of an idealized geothermal reservoir, and for a given reservoir there exists a critical number of hydraulically active fractures beyond which additional fractures provide no increase in lifetime profits. Given initial reservoir characterization data, our model simulates reservoir performance that predicts financial outcomes, which operators can use during initial exploration stage. These predictions are also useful during normal operations to consider strategies, such as drilling make-up wells, to mitigate the effects of production-induced thermal drawdown.

Rate transient analysis of arbitrarily-oriented, hydraulically-fractured media

A semi-analytical mathematical solution to the Heat Equation has been derived to address the issue of rate forecasting from hydraulic fractures in unconventional oil reservoirs. The fracture is assumed to have a time-independent pressure profile along its length with a terminally declining flow rate. The analytical model makes use of the point solution to the heat equation for a constant rate in a bound system of a rectangular geometry. The constant pressure solution is constructed from the constant rate solution through convolution performed in the Laplace domain, then analytic inversion to the time domain. The line source solution is then acquired through analytic integration of the point source solution along a well trajectory. Results are generated using a simulator based on the mathematical model for arbitrarily oriented fractures in a 2D domain. Since the physical problem involves inverse modeling of flow rate data for reservoir characterization, diagnostics techniques for probing the fracture and drainage geometry are presented using derivative analysis of rate transient behavior of hydraulically-fractured media.

Improved estimation of elastic attributes from prestack seismic data for reservoir characterization

Seismic derivable elastic attributes, e.g., elastic impedance, lambda-rho, mu-rho, and Poisson impedance (PI), are routinely being used for reservoir characterization practice. These attributes could be derived from inverted [Formula: see text], [Formula: see text], and density, and usually indicate high sensitivity to reservoir lithology and fluid. Due to the high sensitivity of such elastic attributes, errors or measurement noise associated with the acquisition, processing, and inversion of prestack seismic data will propagate through the inversion products, and will lead to even larger errors in the computed attributes. To solve this problem, we have developed a two-step cascade workflow that combines linear inversion and nonlinear optimization techniques for the improved estimation of elastic attributes and better prediction and delineation of reservoir lithology and fluids. The linear inversion in the first step is an inversion scheme with a sparseness assumption, based on L1-norm regularization. This step is used to select the major reflective layer locations, followed in the second step by a nonlinear optimization process with the predefined layer structure. The combination of these two procedures produces a reasonable blocky earth model with consistent elastic properties, including the ones that are sensitive to reservoir lithology and fluid change, and thus provides an accurate approach for seismic reservoir characterization. Using PI, as one of the target elastic attributes, as an example, this workflow has been successfully applied to synthetic and field data examples. The results indicate that our workflow improves the estimation of elastic attributes from the noisy prestack seismic data and may be used for the identification of the reservoir lithology and fluid.

3D geological modeling of the Upper Cretaceous reservoirs in GPT oil field, Abu Sennan area, Western Desert, Egypt

This study aims to construct 3D geological model using the integration of seismic data with well log data for reservoir characterization and development of the hydrocarbon potentialities of the Upper Cretaceous reservoirs of GPT oil field. 2D seismic data were used to construct the input interpreted horizon grids and fault polygons. The horizon which cut across the wells was used to perform a comprehensive petrophysical analysis. Structural and property modeling was distributed within the constructed 3D grid using different algorithms. The workflow of the 3D geological model comprises mainly the structural and property modeling. The structural model includes fault framework, pillar girding, skeleton girding, horizon modeling and zonation and layering modeling processes. It shows system of different oriented major and minor faults trending in NE–SW direction. The property modeling process was performed to populate the reservoir facies and petrophysical properties (volume of shale (Vsh), fluid saturations (Sw and Sh), total and effective porosities (Φt and Φe), net to gross thickness and permeability) as extracted from the available petrophysical analysis of wells inside the structural model. The model represents a detailed zonation and layering configuration for the Khoman, Abu Roash and Bahariya formations. The 3D geological model helps in the field development and evaluates the hydrocarbon potentialities and optimizes production of the study area. It can be also used to predict reservoir shape and size, lateral continuity and degree of interconnectivity of the reservoir, as well as its internal heterogeneity.

Petrofacies characterization using best combination of multiple elastic properties

Elastic properties such as P-impedance and the ratio of P- to S-wave velocity are key features for linking well log data to seismic data for reservoir characterization. Crossplotting methods from well log data are commonly applied to delineate the distribution of petrofacies on the seismic volume. However, only two or three elastic properties (two- or three-dimensional data) can be used in the crossplotting methods because of data visualization issues. Furthermore, the elastic properties used in the crossplotting method have to be chosen subjectively or empirically by a human interpreter. In this study, we propose a new workflow to overcome the limitations of conventional crossplotting methods. We use more than three elastic properties from well log data and apply non-linear transformations to the properties, to improve separability of the non-linear datasets. Then, we choose the best combination of the non-linear transformed elastic properties using class-scatter-matrix based separability measure. Lastly, Bayesian inference with multivariate probability density function (PDF) of petrofacies is performed to predict petrofacies probability volumes in the seismic area. An application to Vincent oil field data demonstrates the effectiveness of the proposed workflow. The accuracy improved from 80.7% to 86.2% and uncertainty is significantly reduced compared to the conventional workflow with crossplotting methods.

Adaptive Pressure Management and Plume Control in Geological Co2 Storage: Designing a Brine Extraction Field Experiment

Industrial scale injection of CO2 into the subsurface can result in significant fluid pressure increases, which can lead to potential environmental impacts such as caprock fracturing, fault activation and leakage into underground fresh water aquifers. Pressure management through brine extraction is an approach for managing formation pressure, effective stress, and plume movement in response to CO2 injection. However, the handling and management of the extracted brine has a cost that is added to carbon capture and sequestration (CCS) operations; therefore minimizing the extracted volume of brine can be of great importance. At the same time, economics of pressure management via brine extraction can be improved by treating the extracted high-salinity water and making it available for beneficial uses, for example as cooling water for power plants. In this paper, we introduce and demonstrate application of an adaptive reservoir management approach that optimizes extraction rates of reservoir brines for pressure control in an integrated optimization framework. Our approach will be tested during a brine extraction field experiment located in the southern United States, which is currently in the design and planning stages. The objective of the experiment is to evaluate the technical feasibility of managing subsurface pressures associated with large-scale CO2 injection volumes and to assess the cost and effectiveness of desalination technologies for saline waters containing high total dissolved solids (TDS). Our integrated adaptive management approach involves monitoring, model calibration, and optimization of brine extraction for pressure control. Based on measurements obtained during the injection phase and utilizing improved predictive models from repeated calibration, initial optimization calculations will be revised in regular intervals and the operating decisions for controlling and managing subsurface pressurization will be updated. In this paper, in preparation of the actual field demonstration, we investigate use of borehole measurements (i.e., pressure changes) and electromagnetic (EM) geophysical methods (i.e. salinity changes) in the adaptive management of the injection and brine extraction project. We also present a numerical exercise to investigate how the optimization performance is affected by the quality of initial site characterization data and by the frequency of dynamic model updates with newly acquired data during the injection. Our numerical study shows that more accurate initial reservoir characterization data reduce the risk of pressure buildup above a given maximum pressure because the optimization algorithm arrives at better estimates of initial extraction rates, which in turn results in better control of pressure during the overall injection times. Results also show that low frequencies of model calibration and optimization with the new data, especially at early injection periods, may lead to optimization problems, either because pressure buildup constraints are violated or excessively high extraction rates are proposed. These optimization problems can be eliminated if more frequent data collection and model calibration are conducted, especially at early injection times. Approaches such as adaptive pressure management may constitute an effective tool to manage pressure buildup under uncertain reservoir conditions by minimizing the brine extraction volumes while controlling pressure buildups. This will be demonstrated in the actual field study, with injection planned to start in early 2019.

Optimizing compaction calculation for improved probabilistic 2D mapping

ABSTRACTIntegration of all available data in reservoir characterization is critically important. 2D mapping is a reliable and robust technique that allows integration of multiple secondary data, including geological and geophysical surfaces and maps, to generate realistic summaries of reservoir quality at each location in an area of interest with an associated measure of uncertainty. This is achieved in 2D mapping with a more straightforward implementation, requiring significantly less time and fewer resources than three‐dimensional modelling. In this paper, we propose an approach for the empirical calculation and optimization of differential compaction maps by leveraging existing well control for the use in 2D mapping. Success of the proposal is demonstrated through tests of accuracy, precision and fairness of the local uncertainty distributions for 100 new stratigraphical wells drilled in the Christina Lake and Kirby East area.

Significance of core analysis and gamma-ray trends in depositional facies interpretation and reservoir evaluation of Cenomanian sequence, Alam El-Shawish East Oil Field, North Western Desert, Egypt

The present paper aims to evaluate the sedimentological model of the study Cenomanain sequence in terms of electrofacies interpretation and depositional facies architecture to reveal the spatial distribution of the reservoir facies characterization. The influence of depositional lithofacies types on reservoir characterization of the Cenomanian Bahariya Formation and Abu Roash “G” Member was investigated using the integration of available core data and petrophysical evaluation from seven wells in Alam El-Shawish East Oil Field, North Western Desert, Egypt. Careful log interpretation and correlation enable differentiation of the study sequence into ten sedimentary cycles. The stacking of the recognized facies types in the available cored interval, form fifteen depositional facies associations from which fluvial, estuarine and shoreline-shallow shelf models were constructed. This facies information has been used to calibrate the log response, particularly the gamma-ray trends, as an aid to interpret the depositional environment of the non-cored intervals. However, the generalized log pattern shows micro-variations in log shape and trend based on the location relative to depositional environment and consequently major modified gamma-ray patterns were proposed depending on the cored facies architecture. Therefore, precautions must be taken into consideration in extrapolating the present patterns to any similar heterogeneous sequence due to the effects of variable diagenetic and facies type constraints on log response. It is indicated that hydrocarbons could be accumulated within shoreface and barrier tidal inlet complex near or within the depositional sequence boundary surfaces along the Upper Bahariya Member and the upper unit of Abu Roash “G” Member. The associated fine siliciclastics (mudstone and shale) of tidal lagoon and/or shoreface delta facies may be acted as local source rock and trap seals. Based on reservoir characterization data supplemented by well logs interpretation of the cored and non-cored intervals, further reservoir beds could be recommended as potential prospecting targets.

Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF) and ensemble smoother (ES) as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.