• Energy Research

Abstract Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are ubiquitous across the worldwide and are potentially very good source to store carbondioxide (CO2) for a longer period of time. The simulation models are great tool to assess the potential and understanding the physics behind CO2-brine interaction in subsurface reservoirs. Simulating the behavior of fluid flow in NFR reservoirs during CO2 are computationally expensive because of the multiple reasons such as highly-fractured and heterogeneous nature of the rock, fast propagation of CO2 plume in the fracture network, and high capillary contrast between matrix and fractures. This paper presents a data-driven deep learning surrogate modeling approach that can accurately and efficiently capture the temporal-spatial dynamics of CO2 saturation plumes during injection and post-injection monitoring periods of Geological Carbon Sequestration (GCS) operations in NFRs. We have built a physics-based numerical simulation model to simulate the process of CO2 injection in a naturally fractured deep saline aquifers. A standalone package was developed to couple the discrete fracture network in a fully compositional numerical simulation model. Then reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and operational parameters. The simulation model parameters were obtained from extensive geological surveys published in literature. These samples generated a massive physics-informed database (about 900 simulations) that provides sufficient training dataset for the Deep Learning surrogate models. Average Absolute Percentage Error (AAPE) and coefficient of determination (R2) were used as error metrics to evaluate the performance of the surrogate models. The developed workflow showed superior performance by giving AAPE less than 5% and R2 more than 0.95 between ground truth and predictions of the state variables. The proposed Deep Learning framework provides an innovative approach to track CO2 plume in a fractured carbonate reservoir and can be used as a quick assessment tool to evaluate the long term feasibility of CO2 movement in fractured carbonate medium.

Simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing partial-differential equations (PDEs) into algebraic systems via numerical methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint. Therefore, developing cost-effective, data-driven models can become a practical choice since deep learning approaches are considered to be universal approximations. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO2 storage sites, where CO2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained from physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses including the temporal-spatial evolution of the pressure and saturation plumes. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enables development of data-driven models with higher fidelity. 22 pages, 15 figures

The Puga geothermal reservoir is located in the south-eastern part of Ladakh (Himalayan region, India), and it is providing encouraging results towards heat production. We proposed an improved mathematical model for the fully coupled thermo-hydro-geomechanical model to examine the variations in the Puga geothermal reservoir at between 4500 m from the surface with three, four, and seven hydraulic fractures in the reservoir along with four-spot, five-spot, seven-spot, and nine-spot well patterns. The distribution of low-temperature region is found in each fracture, and it is low in the reservoir with seven hydraulic fractures. The changes in the rock and fluid properties are examined effectively. Thermal strain is dominated in the fractures, and mechanical strain is impressive in the rock matrix; it is dependent on the number of hydraulic fractures and well patterns. The thermal performance of the Puga reservoir is examined with the geothermal life, reservoir impedance, and heat power and found that the number of hydraulic fractures and well patterns are influenced significantly in the multistage modeling of the Puga geothermal reservoir. Thus, the proposed mathematical model can effectively evaluate and predict the variations that occur in the Puga geothermal reservoir with dynamic rock, fracture, and fluid properties. ; Manojkumar Gudala and Suresh Kumar Govindarajan gratefully acknowledge financial support from the Indian Institute of Technology–Madras; Manojkumar Gudala and Bicheng Yan thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1423-01-01; Manojkumar Gudala and Shuyu Sun thanks for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01.

Abstract In this work, we studied the implementation of huff and puff technology to extract heat from the geothermal reservoir. Two-dimensional numerical investigations were carried out using a fully coupled two-phase thermo-hydro-mechanical model with dynamic rock and fluid properties. COMSOL Multiphysics (a finite element solver) was utilized to build the model. The CO2 geofluid is injected in a supercritical state in a water-saturated geothermal reservoir. The results were showing promising for the extraction of heat and storing of CO2. In the simulation model, we designed a well pair (two-vertical wells) system with two different operating perforations in the same well with huff and puff cycle operation, and this technology is named as Doublet Huff and Puff (DHP). Injection wells operating at the top of the formation and production wells are operating at the bottom. The injection well-1 and production well-1 are operating at same time (i.e., 2 years). During this period, injection well-2 and production well-2 are ideal, and injection well-1 and production well-1 are ideal while operating injection well-2 and production well-2. This process is continued till the whole reservoir is saturated with the injected CO2 and/or the reservoir temperature reaches 60 % (i.e., geothermal reservoir life) of its original temperature. The CO2 plume expanding throughout the reservoir effectively while extracting heat from the reservoir. The sensitivity of well distance, injection temperature, injection velocity, and perforation length on the production temperature was investigated. The production temperature stays stable and high for a long time and no influence on the production temperature. Thus, the proposed technique (DHP) can be implemented for sequestering large amounts of CO2 along with heat extraction in geothermal reservoirs.

Effectively storing carbon dioxide (CO2) in geological formations synergizes with algal-based removal technology, enhancing carbon capture efficiency, leveraging biological processes for sustainable, long-term sequestration while aiding ecosystem restoration. On the other hand, geological carbon storage effectiveness depends on the interactions and wettability of rock, CO2, and brine. Rock wettability during storage determines the CO2/brine distribution, maximum storage capacity, and trapping potential. Due to the high CO2 reactivity and damage risk, an experimental assessment of the CO2 wettability on storage/caprocks is challenging. Data-driven machine learning (ML) models provide an efficient and less strenuous alternative, enabling research at geological storage conditions that are impossible or hazardous to achieve in the laboratory. This study used robust ML models, including fully connected feedforward neural networks (FCFNNs), extreme gradient boosting, k-nearest neighbors, decision trees, adaptive boosting, and random forest, to model the wettability of the CO2/brine and rock minerals (quartz and mica) in a ternary system under varying conditions. Exploratory data analysis methods were used to examine the experimental data. The GridSearchCV and Kfold cross-validation approaches were implemented to augment the performance abilities of the ML models. In addition, sensitivity plots were generated to study the influence of individual parameters on the model performance. The results indicated that the applied ML models accurately predicted the wettability behavior of the mineral/CO2/brine system under various operating conditions, where FCFNN performed better than other ML techniques with an R2 above 0.98 and an error of less than 3%.

Uncertainties in static and dynamic subsurface parameters are involved in geothermal field modeling. The quantification of such uncertainties is important to guide field-development alternatives and decision-making. This work presents a novel method for estimating thermal recovery and produced-enthalpy rates, combined with uncertainty quantification and optimization. We use time-continuous, multi-objective uncertainty quantification for geothermal recovery by water re-injection. The uncertainty ranges were determined using a database of 135 geothermal fields. Thermal recovery and produced-enthalpy rates are then evaluated as functions of dimensionless uncertainty parameters. Using the proposed method, a set of 25 geothermal fields are analyzed to determine optimal well spacing. This method quantifies time-continuous uncertainty and global sensitivity for geothermal field modeling undergoing re-injection when detailed subsurface data are not available. ; We would like to thank CMG Ltd. for providing the STARS academic license, KAUST for the support, and UQLab for the software’s license.

Abstract In the present work, fully coupled dynamic thermo-hydro-mechanical (THM) model was employed to investigate the advantage and disadvantages of supercritical CO2 (SCCO2) over water as geofluids. Low-temperature zone was found in both SCCO2-enhanced geothermal system (EGS) and water-EGS systems, but spatial expansion is higher in water-EGS. Although, the spatial expansion of SCCO2 into the rock matrix will help in the geo-sequestration, the expansion of stress and strain invaded zones were identified significantly in the vicinity of fracture and injection well. SCCO2-EGS system is giving better thermal breakthrough and geothermal life conditions compared to the water-EGS system. Reservoir flow impedance (RFI) and heat power are examined, and heat power is high in the water-EGS system. Minimum RFI is found in the SCCO2-EGS system at 45 °C and 0.05 m/s. Maximum heat power for SCCO2-EGS was observed at 35 °C, 20 MPa, and 0.15 m/s. Therefore, the developed dynamic THM model is having greater ability to examine the behavior of SCCO2-EGS and water-EGS systems effectively. The variations occur in the rock matrix, and the performance indicators are dependent on the type of fluid, injection/production velocities, initial reservoir pressure, and injection temperature. The advantages of SCCO2-EGS system over the water-EGS system provide a promising result to the geothermal industry as a geofluid.

Abstract The state of the art of modeling fluid flow in shale reservoirs is dominated by dual porosity models which divide the reservoirs into matrix blocks that significantly contribute to fluid storage and fracture networks which principally control flow capacity. However, recent extensive microscopic studies reveal that there exist massive micro- and nano-pore systems in shale matrices. Because of this, the actual flow mechanisms in shale reservoirs are considerably more complex than can be simulated by the conventional dual porosity models and Darcy’s Law. Therefore, a model capturing multiple pore scales and flow can provide a better understanding of complex flow mechanisms occurring in these reservoirs. Through the use of a unique simulator this paper presents a micro-scale multiple-porosity model for fluid flow in shale reservoirs by capturing the dynamics occurring in three separate porosity systems: organic matter (mainly kerogen), inorganic matter, natural fractures. Inorganic and organic portions of shale matrix are treated as sub-blocks with different attributes, such as wettability and pore structures. In the organic matter or kerogen, gas desorption and diffusion are the dominant physics. Since the flow regimes are sensitive to pore size, the effects of nanopores and vugs in kerogen are incorporated into the simulator. The separate inorganic sub-blocks mainly contribute to the ability to better model dynamic water behavior. The multiple porosity model is built upon a unique tool for simulating general multiple porosity systems in which several porosity systems may be tied to each other through arbitrary transfer functions and connectivities. This new model allows us to better understand complex flow mechanisms and in turn is extended into the reservoir scale considering hydraulic fractures through upscaling techniques. Sensitivity studies on the contributions of the different flow mechanisms and kerogen properties give some insight as to their importance. Results also include a comparison of the conventional dual porosity treatment and show that significant differences in fluid distributions and dynamics are obtained with the improved multiple porosity simulation. Finally a case for reservoir-scale model covering organic matter, inorganic matter, natural fractures and hydraulic fractures is presented and will allow operators to better predict ultimate recovery from shale reservoirs.

Abstract Conventional compositional simulators are usually difficult to interpret the different gas oil ratio (GOR) from tight oil reservoirs, and this also indicates an unreliable prediction of ultimate hydrocarbon recovery. We realize that there are two issues related to the compositional simulation of production in tight oil reservoirs. Firstly, tight oil reservoirs typically exhibit extremely small matrix pore size in the order of nanometers, so the capillary pressure between vapor and liquid phases is considerable such that the PVT of the confined fluid deviates from that of the bulk fluid with capillary pressure ignored. Secondly, during depletion process, rock compaction causes pore space reduction and brings remarkable changes in rock properties. In this work we implement rigorous confined fluid phase behavior calculation depending on capillary pressure and rock compaction in a fully compositional simulator. Capillary pressure in matrix nanopores is calculated by Leverett J-function. Further, the impact of capillarity on phase equilibrium is taken into account through modifying the stability test and two-phase flash calculation. Dynamic rock compaction is considered in the simulator via rock compaction tables, such that fluid mobility decreases with permeability reduction and capillary effect is simultaneously coupled. The unique implementation in the simulator captures the dynamic behavior of rock and fluid properties in tight oil reservoirs. Typical suppression of bubble point pressure and reduction of oil viscosity and density is observed from our simulation results. Reservoir-scale simulation results show that this model resolves the problem of the inconsistent GOR in tight oil production and greatly facilitates the history matching process. The enhanced compositional simulation will ultimately improve our understanding of tight oil reservoirs and provide better guidance for recovery prediction.