Reservoir Performance Prediction Research Articles

Analysis of carbon dioxide reservoir performance and optimization strategies: a case study of a carbon dioxide plant

This study aimed at developing an empirical model for estimating and analyzing CO2 reservoir performance. This was achieved by using decline curve analysis (DCA) to determine an accurate empirical decline model for reservoir performance prediction and optimal producing, i.e., time at which the plant recovery build-up (RBU) efficiency has declined to an approximate value of 44.5%. MATLAB R2013b was used for calculating raw gas density whereas Ms Excel 2007 was used in plotting and performing all calculations. Through an examination of real production data, a harmonic decline model with an Arps’ decline constant (d) of 1.0 and a nominal decline rate of approximately 0.035572337/year was identified as the most suitable for characterizing reservoir performance. The study identifies the year 2048 as an optimal opportunity for implementing production optimization strategies to maximize returns while ensuring efficient resource recovery. Acknowledging the limitations of DCA, the study recommends exploring alternative forecasting models such as type curve analysis (TCA), material balance equation (MBE), and well simulation techniques to mitigate uncertainties in reservoir performance prediction. In addition, the study proposes a 5-year model verification and improvement program to refine reservoir descriptions and enhance data acquisition practices, aiming to optimize production efficiency and minimize uncertainties in CO2 reservoir operations.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

High Resolution Strontium Isotope Stratigraphy of L-III Reservoir of Mumbai High and adjoining Cluster 7 areas of Mumbai Offshore Basin, India

ABSTRACTHigh degree of reservoir heterogeneity has always been problematic in the identification and chronostratigraphic mapping of individual layers in a thick limestone sequence, especially when there is poor preservation of index fossils for thin sub-layers in the sequence. The multi-layered Miocene L-III reservoir of Bombay Formation in Mumbai High and adjoining southwestern area of the Mumbai offshore basin of India have suffered from similar issues due to lack of absolute ages and, therefore, the lack of highresolution chronostratigraphy for its individual thin limestone layers. The present study attempts to establish high-resolution chronostratigraphic age constraints of different sub-units, namely A, B, C, D, and E layers, of the L-III unit, using strontium isotope stratigraphy of carbonate (core and cutting) samples cut from 7 wells in Mumbai High and five wells in Cluster-7 area southwest to Mumbai High. The study provides a geochronological age range of ~2.1 Ma for the L-III unit, with the top of L-III in Mumbai High South assigned at 16.6 Ma based on Strontium isotopic analysis. The study has further helped in assigning absolute ages of individual layers of the L-III reservoir, with the A1 sub-unit at 16.6-16.8 Ma, A2 between 16.9-17.9 Ma, B sub-unit at 18.0 Ma and C & D sub-units between 18.2-18.3 Ma. Layer E of the L-III unit was dated to be 18.5-18.6 Ma, and the L-IV top has been assigned to be at 18.7 Ma. The study also correlated the ages obtained in the Mumbai High region with the studied wells southwest of Mumbai High and provided high-resolution strontium isotopic chronostratigraphy for equivalent sub-layers A to E in the Cluster-7 area SW to Mumbai High. These ages have provided new numerical age constraints for the L-III and its sub-units for the first time, are correlatable across different well sections, and are in perfect agreement with reported biostratigraphic ages for the Bombay Formation and the sub-units within. This has important implications for effective reservoir management and performance prediction for the hydrocarbon-producing pays in the studied L-III reservoir unit.

IORSim: A Mathematical Workflow for Field-Scale Geochemistry Simulations in Porous Media

Reservoir modeling consists of two key components: the reproduction of the historical performance and the prediction of the future reservoir performance. Industry-standard reservoir simulators must run fast on enormous and possibly unstructured grids while yet guaranteeing a reasonable representation of physical and chemical processes. However, computational demands limit simulators in capturing involved physical and geochemical mechanisms, especially when chemical reactions interfere with reservoir flow. This paper presents a mathematical workflow, implemented in IORSim, that makes it possible to add geochemical calculations to porous media flow simulators without access to the source code of the original host simulator. An industry-standard reservoir simulator calculates velocity fields of the fluid phases (e.g., water, oil, and gas), while IORSim calculates the transport and reaction of geochemical components. Depending on the simulation mode, the geochemical solver estimates updated relative and/or capillary pressure curves to modify the global fluid flow. As one of the key innovations of the coupling mechanism, IORSim uses a sorting algorithm to permute the grid cells along flow directions. Instead of solving an over-dimensionalized global matrix calling a Newton–Raphson solver, the geochemical software tool treats the species balance as a set of local nonlinear problems. Moreover, IORSim applies basis swapping and splay tree techniques to accelerate geochemical computations in complex full-field reservoir models. The presented work introduces the mathematical IORSim concept, verifies the chemical species advection, and demonstrates the IORSim computation efficiency. After validating the geochemical solver against reference software, IORSim is used to investigate the impact of seawater injection on the NCS Ekofisk reservoir chemistry.Article HighlightsThe IORSim sorting algorithm decouples the nonlinear geochemical reaction calculations into recurring one-dimensional problems to assure numerical stability and computation efficiency. To the best of our knowledge, this work presents the mathematical concept, implementation, and application of topological sorting for the first time on (industry) field-scale problems.IORSim combines topological sorting with basis swapping and splay trees to significantly reduce computation times. Moreover, a high-speed forward simulation mode was developed to allow the post-advection of chemical components to visualize species distribution, water chemistry, and mineral interactions. If the geochemical reactions interfere with the fluid flow, the IORSim backward mode uses relative permeability curves to update the global fluid flow at each time step.We validate the implemented topological scheme on a reservoir grid, show the computation efficiency, and compare the impact of explicit, implicit, and grid refinement on numerical dispersion.The decoupled flow simulator and geochemical reaction calculations allow seamless integration of full-field reservoir models that contain complex geological structures, a large number of wells, and long production histories.The computation capabilities of IORSim are demonstrated by simulating and reproducing the impact of seawater injection in the southern segment of the giant Ekofisk field (more than 50 years of injection and production history). IORSim shows that seawater injection changed the Ekofisk mineralogy and impacted the produced water chemistry. In the investigated Ekofisk case, seawater promoted calcite dissolution and led to the precipitation of magnesite and anhydrite. Moreover, surface complexation modeling revealed that sulfate is adsorbed on the calcite surface.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

When considering multiphase flow scenarios, the interpretation of petrophysical properties poses significant challenges for production forecasts and reservoir modeling. The findings of the numerical modeling were therefore subject to uncertainty because characteristics like relative permeability and capillary pressure curve were hardly ever bound by interpretations. The uncertainty may result in inaccurate predictions of reservoir performance and skewed perceptions of the reservoir. Due to the difficulty in directly interpreting such property from the available field data and the expensive cost of coring, analyses or experimental measurements to determine relative permeability and capillary pressure were infrequently carried out. Such a gap would be filled by a straightforward yet rigorous method. In this study, we develop production projections for a wide range of three-phase compositional volatile oil reservoirs. Then, we used an artificial neural network to figure out how petrophysical characteristics and production data relate to one another. The artificial neural network model was adjusted, and the final trained model was tested blindly to determine how well it predicted permeability, multiphase relative permeability, and capillary pressure data. For the testing scenarios, consistency is seen between the predicted values and the original ones, despite some mispredictions being present. To provide production projections that can be compared to those from the reservoir model that include the initial petrophysical characteristic, the anticipated properties are then propagated into reservoir models. The comparison findings show that for 65/59/34 out of 74 testing scenarios, the reservoir model with artificial neural network-predicted features can anticipate oil/gas/water output with < 20% inaccuracy. With the developed artificial neural network tool, the reservoir engineers can evaluate the three-phase relative permeability surface from rate-transient data conveniently improving the accuracy of the relative permeability data implemented by history matching or from core experiments which sometimes are extremely expensive. The findings of this study can help for a better understanding of the relationships between three-phase rate-transient data and the relative permeability surface as well as the horizontal/vertical permeability.

Well Production Decline Models Considering Mechanical Skin Factor

Production decline analysis is an essential tool in predicting reservoir performance and in estimating reservoir properties. In its most basic form, decline curve analysis is to a large extent based on Arps’ empirical models that have little theoretical basis. But the mechanical skin factor due to formation damage is not taken into account in the production decline models available in the literature. The conventional models presented in the literature are mostly empirical and not grounded in fundamental in theory. New theoretical models are developed in this paper to forecast the production decline of a well producing in a closed circular or rectangular reservoir at constant bottomhole pressure, and mechanical skin factor due to formation damage is taken into account. Both the transient flow behavior and boundary‐dominated flow behavior are incorporated; the effects of reservoir size, well location, and mechanical skin factor on transient flow rate and pressure drop in the reservoir are studied. Analytical solutions are obtained through combinations of Green’s function and inverse Laplace transform. Stehfest’s algorithm is used to convert the obtained solution from the Laplace transform domain into the real time domain. The proposed models in this paper are based on analytical solutions to diffusivity equations in porous media, and the solutions are fast and practical tools to forecast the production performance of a well producing at constant flowing bottomhole pressure.

A Hybrid Data-Physics Framework for Reservoir Performance Prediction with Application to H2S Production

Summary Model-based reservoir management workflows rely on the ability to generate predictions for large numbers of model and decision scenarios. When suitable simulators or models are not available or cannot be evaluated in a sufficiently short time frame, surrogate modeling techniques can be used instead. In the first part of this paper, we describe extensions of a recently developed open-source framework for creating and training flow network surrogate models, called FlowNet. In particular, we discuss functionality to reproduce historical well rates for wells with arbitrary trajectories, multiple perforated sections, and changing well type or injection phase, as one may encounter in large and complex fields with a long history. Furthermore, we discuss strategies for the placement of additional network nodes in the presence of flow barriers. Despite their flexibility and speed, the applicability of flow network models is limited to phenomena that can be simulated with available numerical simulators. Prediction of poorly understood physics, such as reservoir souring, may require a more data-driven approach. We discuss an extension of the FlowNet framework with a machine learning (ML) proxy for the purpose of generating predictions of H2S production rates. The combined data-physics proxy is trained on historical liquid volume rates, seawater fractions, and H2S production data from a real North Sea oil and gas field, and is then used to generate predictions of H2S production. Several experiments are presented in which the data source, data type, and length of the history are varied. Results indicate that, given a sufficient number of training data, FlowNet is able to produce reliable predictions of conventional oilfield quantities. An experiment performed with the ML proxy suggests that, at least for some production wells, useful predictions of H2S production can be obtained much faster and at much lower computational cost and complexity than would be possible with high-fidelity models. Finally, we discuss some of the current limitations of the approach and options to address them.

Data Analytics Enables Reconstruction of Missing Segments in Well Data History

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 208137, “Reconstruction of Missing Segments in Well Data History Using Data Analytics,” by Yuanjun Li, SPE, and Roland Horne, SPE, Stanford University, and Ahmed Alshmakhy, SPE, ADNOC, et al. The paper has not been peer reviewed. _ The problem of missing data is a common one in well-production records. An incomplete data set is commonly simplified by omitting all observations with missing values, which can lead to significant information loss. In the complete paper, the authors developed an efficient procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables. Ultimately, the complete information can support the reservoir history-matching process and production allocation and can develop models for reservoir performance prediction. Introduction Missing values are a prevalent issue in pressure transient analysis. Their occurrence is often unavoidable and is a serious problem because many data-mining algorithms cannot work with data sets that are missing values. Sources of missing field data include operational problems, changes in operating conditions, and variable sampling frequencies. Imputation Technique of Missing Values Case Deletion. If a missing data problem exists in a multivariate series, the time series may not be evaluated appropriately. In time-series data, each record is unique. Thus, deleting a record would result in a series with gaps, which would be unusable for many analyses. Furthermore, the time-series plot would be truncated and riddled with flaws. Thus, to conduct a time-series analysis, those missing observations must be estimated and reinserted. When the number of missing values in the data set is considerable, eliminating the associated cases might result in the loss of vital and valuable information. Researchers have found that case elimination is not appropriate when the missing rate is greater than 3%. Physics-Based Imputation. Any physics-driven diagnostic or model is based on fundamental assumptions that may or may not be correct in all situations but are intended to represent reservoir behavior. While data-driven models do not make any assumptions about underlying physics, they rely on training data. Therefore, data analytics and machine learning can be an ideal substitute. Local Statistical Approach. Local statistical methods include the K-nearest-neighbor (KNN) imputation and local-least-square imputation, two of the most well-known algorithms. To impute the incomplete data, the KNN imputation employs a pairwise correlation between the target gene with missing values and the K closest reference genes. For missing-value imputation, the techniques in this local method use local similarity patterns in the data sets or a specific column of data.

Bridging Computational Stratigraphy and Reservoir Simulation for Geologically Realistic High-Resolution Reservoir Modeling

Summary Computational stratigraphy (CompStrat) is a state-of-the-art Earth-modeling method that captures the key heterogeneities in subsurface reservoirs through modeling of the detailed flow and sediment transportation processes in various depositional environments. The method is fully based on physics and generates high-resolution 3D Earth models that are much more geologically realistic than those generated by traditional Earth-modeling methods. It can accurately predict and preserve those spatially continuous but vertically thin and volumetrically insignificant layers, such as shale layers, thus enabling a much more accurate representation of natural reservoir connectivity. In the past few years, CompStrat has been studied mainly within the Earth science community and has not been broadly applied yet in reservoir simulation research and practices. Our objective is to bridge this gap and allow this frontier technology to offer geologically realistic Earth models for reservoir simulation to better understand how various geological features contribute and control subsurface flow patterns and performance, subsequently leading to better integration among Earth modeling, flow simulation, and more reliable reservoir performance predictions. CompStrat models often have large numbers of cells (hundreds of millions or more). A large proportion of them are related to thin shale layers. These thin cells can often cause convergence difficulties in reservoir simulations. We developed a grid coarsening method to dramatically reduce the cell number and the simulation time with minimal alteration of the overall model connectivity characteristics. The method reduces the cell number by 85% (to 93%) and the simulation time by 94% (to 99%) with limited loss of accuracy for representative examples. Without this method, the simulation may take impractically long time to run for large models with complex multiphase flow dynamics. The successful removal of the computational bottleneck enables the application of this frontier Earth-modeling method in high-fidelity reservoir simulation. It also facilitates a detailed understanding of the connection between geology and flow to offer valuable insight for reservoir modeling, production forecast uncertainty analysis, and history matching. We developed a method to label, evaluate, and rank geological features based on their influence on flow performance, with shale layers being the specific focus. The labeling is performed semiautomatically, and the evaluation and ranking are done efficiently with a reduced-physics solver. The result is statistically consistent across multiple realizations. The fluvial-deltaic CompStrat sector model on which our grid coarsening and shale analysis methods were tested is shared in the Supplementary Materials.

Geophysical Modeling and Its Contribution on the Reservoir Characterization of Al Baraka in El Gallaba Plain, South Egypt

Summary The early Cretaceous formations in recent years are considered significant potential hydrocarbon-bearing rocks in many rift basins such as Komombo, south Egypt. Therefore, this study is focused on the critical analysis and interpretation of well logging together with seismic reflection data on the Al Baraka petroliferous reservoir in the Komombo subbasin. The interpretation of these data was used to construct the first 3D geophysical models in this area which were subsequently interpreted in terms of their potential to be hydrocarbon-bearing or not. The 3D petrophysical models were deduced to illustrate the spatial distribution and propagation of the petrophysical properties (laterally and vertically) within the reservoir. Additionally, 3D seismic models were prepared to get a comprehensive, in-depth picture of how the productive hydrocarbon reservoir zones are structurally controlled in different depths. So, these models are crucial for explaining reservoir characteristics and providing supported geological reservoir models for precise reservoir performance prediction. This study aims to differentiate and determine hydrocarbon potential zones in terms of the petroleum system. The results of these progressive analyses showed that only two zones (C and D) in the Six Hills Formation are considered the most productive zones because they have a large thickness of sand bodies, low-water saturation values, high porosity, and high permeability. These zones are located in the northeastern and central parts of the studied area, which represent the depocenter of the subbasin. This evidence supported and confirmed the presence of petroleum accumulations in certain zones within the Six Hills Formation. Therefore, this work can give and encourage experts with adequate knowledge to understand the development of the rift basins in Komombo and other basins in middle and south Egypt.

Water Influx Impact on Oil Production in Hamzeh Oil Reservoir in Northeastern Jordan: Case Study

This paper was conducted to delimit the water influx in the Hamzeh oil reservoir, located in northeastern Jordan approximately 150 km east of Amman. Petroleum reservoirs are frequently encompassed by water aquifers that back up the reservoir pressure through water inflow. When the pressure declines in a petroleum reservoir, the water aquifer responds by providing an influx of water. Gradually, the damage is reduced and then eliminated, and more oil is produced from the reservoir. The material balance equation (MBE) is used as the fundamental method for this study, predicting reservoir performance for a period of 11 years. The results for this study prove that the reservoir has a water drive mechanism and that the original oil in place (OOIP) was 24,958,290 m3. The projected oil recovery factor ranges from 10.9 to 25 percent for the Hummar and Shueib formations, respectively, depending on the areal efficiency assumed in the calculations. The water influx for the 11-year period was predicted by an MBE, an unsteady-state model, and the results of the performance reservoir.

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Carbonate excess permeability in pressure transient analysis: A catalog of diagnostic signatures from the Brazil Pre-Salt

Carbonate excess permeability in pressure transient analysis: A catalog of diagnostic signatures from the Brazil Pre-Salt

Comparative Study of In-Situ Combustion Tests on Consolidated and Crushed Cores

Summary A significant amount of oil is contained in carbonate reservoirs, but only half of that oil can be produced by secondary enhanced oil recovery (EOR) methods. However, substantial improvements were made in EOR techniques and the prediction of carbonate reservoir performance within the last decades. Nevertheless, existing flow-simulation computer programs failed to provide a reliable prediction of such reservoirs due to their high heterogeneity and the reactivity of the rock. Potentially, in-situ combustion (ISC) is considered effective in developing heavy oils in carbonate reservoirs. The combustion reactions between crude oil and heterogeneous rock matrices introduce additional complexity to the simulation process. Also, most of the laboratory experiments studying the reaction kinetics of the ISC process are performed on the crushed core. However, to minimize the risks, improve the control of the process, and overcome upscaling issues, physical simulation must be carried out under conditions as close to the reservoir as possible. Consolidated core material in combustion tube (CT) experiments is desirable for better simulating some reservoir conditions with synthetic packs and for the cases when actual preserved reservoir core material may be available. Studying the relative effects of porosity and packing properties (specific surface area, sand grain distribution, and cementation) on the fuel is essential to evaluating the process under actual field conditions. This work presents a set of medium-pressure CT (MPCT) tests on crushed and consolidated cores and analyzes the differences, limitations, and performances of both approaches. Two MPCT tests were performed to evaluate the ISC feasibility on the heavy-oil carbonate reservoir with an initial oil saturation level of 0.38 to 0.50. According to previously published experimental results, such oil saturation levels can help avoid oil banking. Both experiments were conducted at reservoir conditions to consider the phase behavior at elevated pressures and temperatures. The method used in this research allows approbation of the methodology of ISC tests using consolidated core at high pressure, ensuring pack and process integrity during the experiment. The influence of consolidated core caused by significantly lower porosity and more uniform porous media elements than those made with unconsolidated material on combustion performance was assessed. Valuable data for different variations of combustion experiments were generated. This work compared two tests and presented the combustion parameters for a stabilized combustion period, such as fuel and air requirements, recovery efficiency, front velocity, and composition of produced gases. The research intends to demonstrate the potential application problems and address issues that might arise during ISC application on target reservoirs, including the higher air flux required for lower porosity of consolidated core samples. The experimental results performed under conditions closest to reservoir conditions are essential for further predictions and affect the ISC performance during pilot tests.

Building 1D and 3D static reservoir geomechanical properties models in the oil field

Reservoir geomechanical models provide valuable information for various applications ranging from the prediction of surface subsidence to the determination of pore pressure and induced stress changes, wellbore stability, fault reactivation, and caprock integrity. Three-dimensional geological modeling of reservoir geomechanics is an essential tool to predict reservoir performance by considering the geomechanics effects. Thus, this study focuses on the application of 3D static reservoir geomechanical model workflow by using 3-D seismic and well log data for proper optimization in the Volve oil field, Norway. 3D Seismic data are applied to generate the interpreted horizon grids and fault polygons. The horizon which cut across the nine wells is used for the detailed topographic analysis. The workflow includes 1D geomechanical and petrophysical models which are calculated at well locations by using log data. Structural and property modeling (pore pressure, vertical and horizontal stresses, elastic properties, porosity, permeability, and hydrocarbon saturation) is distributed by geostatistical methods such as Kriging and Gaussian. This study indicates the effectiveness of the three-dimensional static modeling technique as a tool for better understanding of the spatial distribution of reservoir geomechanical properties, hence, providing a framework for analyzing future activities in the reservoir such as proposal position and trajectory of new wells for future field development and assessing arbitrary injection-production schedules.

A Machine Learning Approach to Real-Time Uncertainty Assessment of SAGD Forecasts

Summary Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive, and the nonuniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler’s model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many steam-assisted gravity drainage (SAGD) well pairs tends to deviate from a set of predefined type curves. Historical well data is a digital asset that can be utilized to develop machine learning (ML) or data-driven models for production forecasting. These models involve lower computational effort than numerical simulators and can offer better accuracy compared to proxy models based on Butler’s equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and “What If” scenario analysis due to their lower computational cost. This paper presents a novel ML workflow that includes a predictive model development using the random forest algorithm, clustering (to group well pairs by geological properties), Bayesian updating, and Monte Carlo sampling (for uncertainty quatification) for the forecasting of real-world SAGD injection and production data. The training data set consists of field data from 152 well pairs, including approximately 3 years of operational data. Each well pair’s data set involves data that are typically available for an SAGD well pair (e.g., operational data, geological, and well design parameters). This ML workflow can update predictions in real time and be applied for quantifying the uncertainties associated with the forecasts, making it an important step for development planning. To the best of the author’s knowledge, this is the first time ML algorithms have been applied to an SAGD field data set of this size.

The Importance of Physiochemical Processes in Decarbonisation Technology Applications Utilizing the Subsurface: A Review

The Earth’s subsurface not only provides a wide range of natural resources but also contains large pore volume that can be used for storing both anthropogenic waste and energy. For example, geothermal energy may be extracted from hot water contained or injected into deep reservoirs and disused coal mines; CO 2 may be stored within depleted petroleum reservoirs and deep saline aquifers; nuclear waste may be disposed of within mechanically stable impermeable strata; surplus heat may be stored within shallow aquifers or disused coal mines. Using the subsurface in a safe manner requires a fundamental understanding of the physiochemical processes which occur when decarbonising technologies are implemented and operated. Here, thermal, hydrological, mechanical and chemical perturbations and their dynamics need to be considered. Consequently, geoscience will play a central role in Society’s quest to reduce greenhouse gas emissions. This contribution provides a review of the physiochemical processes related to key technologies that utilize the subsurface for reducing greenhouse gas emissions and the resultant challenges associated with these technologies. Dynamic links between the geomechanical, geochemical and hydrological processes differ between technologies and the geology of the locations in which such technologies are deployed. We particularly focus on processes occurring within the lithologies most commonly considered for decarbonisation technologies. Therefore, we provide a brief comparison between the lithologies, highlighting the main advantages and disadvantages of each, and provide a list of key parameters and properties which have first order effects on the performance of specific rock types, and consequently should be considered during reservoir evaluation for decarbonising technology installation. The review identifies several key knowledge gaps that need to be filled to improve reservoir evaluation and performance prediction to be able to utilize the subsurface efficiently and sustainably. Most importantly, the biggest uncertainties emerge in prediction of fracture pattern development and understanding the extent and timescales of chemical reactions that occur within the decarbonising applications where external fluid or gas is cyclically injected and invariably causes disequilibrium within the system. Furthermore, it is clear that whilst geoscience can show us the opportunities to decarbonise our cities and industries, an interdisciplinary approach is needed to realize these opportunities, also involving social science, end-users and stakeholders.

Fast prediction of methane adsorption in shale nanopores using kinetic theory and machine learning algorithm

Fast prediction of methane adsorption in shale nanopores using kinetic theory and machine learning algorithm

An analytical model for performance prediction of miscible flooding methods

An analytical model for performance prediction of miscible flooding methods