Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)

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  • Article

    Deep learning-assisted Bayesian framework for real-time CO2 leakage locating at geologic sequestration sites

    (Elsevier BV, 2024-03) He, Xupeng; Zhu, Weiwei; Kwak, Hyung; Yousef, Ali; Hoteit, Hussein; Energy Resources and Petroleum Engineering Program; Physical Science and Engineering (PSE) Division; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Saudi Aramco, Dhahran, Saudi Arabia; Chinese Academy of Sciences, Beijing, China

    Accurate and efficient localization of CO2 leakage if occurred in subsurface formations, is of significant importance in achieving secure geological carbon sequestration (GCS) projects. However, this task is inherently challenging due to the considerable uncertainties in the subsurface. In this work, we develop a novel deep learning-assisted Bayesian framework for identifying potential CO2 leakage sites based on the reservoir pressure transient behavior measured at the wellbores of injection or observation wells. The method consists of two essential steps: 1) Deep learning surrogate: This step aims to effectively replace the intensive high-fidelity simulation with an efficient deep learning surrogate. 2) Bayesian inversion: In this step, the posterior distributions of potential CO2 leakage locations are inverted, in which the surrogate serves as the forward model. The above two processes are automated using Bayesian optimization instead of a labor-intensive trial-and-error approach. The proposed framework is verified using a 3D geological model simulating CO2 sequestration into a brine-filled reservoir. The results demonstrate the Bayesian-optimized surrogate could successfully capture the underlying process of subsurface CO2-brine flow. The Bayesian inversion algorithm enables localizing CO2 leakage with high accuracy. To our knowledge, the proposed Bayesian framework is implemented for the first time to locate multiple leakage sites at the field scale. The proposed workflow provides an accurate and efficient approach to detecting possible CO2 leakage locations in a real-time manner and has promising potential for field-scale GCS applications.

  • Article

    An efficient fully Crouzeix-Raviart finite element model for coupled hydro-mechanical processes in variably saturated porous media

    (Elsevier BV, 2024-02-23) Guo, Lingai; Younes, Anis; Fahs, Marwan; Hoteit, Hussein; Energy Resources and Petroleum Engineering Program; Physical Science and Engineering (PSE) Division; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Institut Terre et Environnement de Strasbourg, Université de Strasbourg, CNRS, ENGEES, UMR 7063, 5 rue René Descartes 67084 Strasbourg, France

    Coupled hydro-mechanical processes in variably saturated porous media can be encountered in several applications in geosciences and soil mechanics. They are ruled by highly nonlinear and strongly coupled equations of fluid flow in unsaturated porous media and quasi-static mechanical deformation. In this work, we develop an efficient and robust numerical scheme for unsaturated poroelasticity that allows for balancing computational requirements and accuracy. The spatial discretization is based on the Crouzeix-Raviart (CR) finite element method for both the displacement field and the hydraulic head. The CR method is locally mass conservative and uses low-order approximation, which alleviates the computational burden. The time discretization of the coupled nonlinear hydraulic mechanical equations is performed using high order time integration methods and efficient time stepping schemes via the method of lines (MOL).

    Four test cases are investigated to highlight the efficiency and accuracy of the developed numerical scheme. The first test case deals with the settlement induced by gravity in the case of saturated and unsaturated soils. A new semi-analytical solution is developed for the settlement in the unsaturated case and used for the validation of the numerical scheme. The second test case is the Cantilever bracket problem, extended here to variably saturated media and studied to investigate the nonphysical oscillations of the pressure variable with different numerical schemes. The results are compared to finite element solutions obtained with COMSOL with either linear or quadratic approximation of the displacement field. The third test case deals with the deformation of an unsaturated soil under partial infiltration and surface load. This test case is simulated to investigate the accuracy and efficiency of the CR scheme for the challenging problem of infiltration into dry soils. The last test case is simulated to show the applicability of the developed model to a realistic problem dealing with the stability of a hillslope under intense rainfall periods.

  • Conference Paper

    Prediction of Pure Mineral-H2-Brine Wettability Using Data-Driven Machine Learning Modeling: Implications for H2 Geo-Storage

    (IPTC, 2024-02-12) Ali, Muhammad; Tariq, Zeeshan; Mubashir, Muhammad; Kamal, Muhammad Shahzad; Yan, Bicheng; Hoteit, Hussein; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Advanced Membranes and Porous Materials Research Center; Energy Resources and Petroleum Engineering Program; Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Greenhouse gases, particularly carbon dioxide (CO2), have the effect of raising the Earth's temperature. To combat this issue and reduce carbon emissions, it is advisable to shift towards the widespread utilization of cleaner fuels, such as hydrogen. The establishment of a global-scale hydrogen economy, coupled with hydrogen geological storage, presents a viable solution to meet the world's energy demands while accommodating peak usage periods. In geological hydrogen (H2) storage, the rock formation wetting characteristics are essential to regulate fluid dynamics, injection rates, the spread of gas within the rock matrix, and safety considerations. The wetting characteristics of minerals within the rock are significantly influenced by geological factors. To assess the wetting behavior of a mineral/H2/brine system under geo-storage conditions, innovative approaches have emerged. This research utilized a combination of advanced machine learning models, such as fully connected neural networks, adaptive gradient boosting, random forests, decision trees, and extreme gradient boosting to forecast the wettability characteristics of a ternary system comprising hydrogen (H2), brine, and specific rock minerals (namely quartz and mica). The predictions were made under various conditions, including different pressures ranging from 0 to 25 MPa, temperatures spanning from 308 to 343 K, and salinities of 10 wt.% NaCl solution. The machine learning models demonstrated remarkable accuracy in predicting mineral/H2/brine system's wettability (contact angles, advancing and receding). Incorporation of various experimental values have established correlations based on ML techniques. The performance and reliability of these models were rigorously assessed using statistical methods and graphical analyses. The deployed ML models consistently provided accurate predictions of wettability across diverse operational scenarios. Notably, the suggested model exhibited a root mean square error (RMSE) of 0.214 during training and 0.810 during testing. Furthermore, sensitivity analysis revealed that pressure exerted the most significant influence on mineral/H2/brine system's wettability. These ML model outcomes can be effectively utilized to anticipate hydrogen geological storage capacities and ensure the security of restraint in large-scale developments.

  • Conference Paper

    A Data-Infused Methodology for Estimating Swelling Potential in Shales Exposed to Various Completion Fluids

    (IPTC, 2024-02-12) Khan, Mohammad Rasheed; Tariq, Zeeshan; Murtaza, Mobeen; Yan, Bicheng; Kamal, Muhammad Shahzad; Mahmoud, Mohamed; Abbasi, Asiya; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); Physical Science and Engineering (PSE) Division; Energy Resources and Petroleum Engineering Program; Slb; King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; Halliburton, Stavanger, Norway

    Formation damage in reservoirs poses a recurring challenge throughout the phases of drilling, completion, and production, significantly impeding efficiency and diminishing resource extraction in oil and gas development. This detrimentally affects production capacity, leading to potential reservoir shutdowns and hindering the timely discovery and development of oil and gas fields. The water-based drilling fluids are mixed with various swelling inhibitors; nevertheless, shale swelling could still take place during the completion phase as these fluids do not usually consider this phenomenon. To quantify the swelling inhibition potential of drilling/completion fluids, several laboratory experiments are usually carried out. These experiments are costly, time-consuming, and tedious. This study used machine learning technique to predict the dynamic linear swelling of shale wafers treated with different types of completion fluids containing varying inorganic salts such as NaBr, CaBr2, and NH4Q. A comprehensive experimental investigation was conducted to gather datasets suitable for training machine learning model based on various completion fluid constituents. The study involved utilizing a dynamic linear swell meter to quantify swelling inhibition potentials, assessing sodium bentonite clay wafers' responses to all completion fluid solutions through linear swell tests lasting 24 to 48 hours. Additionally, the study measured zeta potential and conductivities across solutions with different concentrations. Leveraging sequential data and memory cell architectures, the research developed an LSTM (Long Short-Term Memory) machine learning model aimed at predicting and comprehending swelling behaviors within specific contexts. This model was trained using input parameters such as zeta potential, salt conductivity, salt concentrations, density, and elapsed time, while the model output represented dynamic linear swelling in percentage. This intelligent technique can be used to guide and streamline laboratory experiments to determine dynamic linear swelling of shales. It can serve as a quick tool to guide fluid engineers at the rig site to delineate shale swelling reasons pre-, post-, and during completion operations. Consequently, operators will be better prepared to deal with unknown swelling issues that lead to NPT in operations.

  • Conference Paper

    Underground Hydrogen Storage in Saudi Arabia: Opportunities and Challenges

    (IPTC, 2024-02-12) Alanazi, Amer; Ye, Jing; Afifi, Abdulkader M.; Hoteit, Hussein; Energy Resources and Petroleum Engineering Program; Physical Science and Engineering (PSE) Division; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)

    Hydrogen (H2) is anticipated to play a crucial role in Saudi Arabia's transition to a low-carbon economy as an alternative clean fuel. The conversion of fossil fuels through steam methane reformation produces blue H2, with captured carbon dioxide (CO2) being stored in geological formations. Saudi Arabia's strategic location and recent policies promote renewable energy and green H2. However, establishing an industrial-scale H2-based economy necessitates a suitable large-scale storage solution. Underground hydrogen storage (UHS) emerges as a prominent option, offering significant storage capacities in the Giga- and Terra-Watt-hour range, effectively addressing seasonal fluctuations in supply and demand from renewables. Therefore, the present work aims to evaluate the opportunity of UHS in Saudi Arabia and assess potential geological formations (salt caverns, deep saline aquifers, and hydrocarbon reservoirs) and key technical challenges to be addressed for UHS integration in the energy grid. This includes criteria for site selection, storage capacity calculations, and other critical scientific research areas to be studied. The paper reviews the geological settings in Saudi Arabia that are potentially suitable for UHS, Red Sea basins, and sedimentary formations in the eastern basins at the Arabian plate. The results highlight the requisite fundamental experimental and numerical studies for a complete understanding of H2/brine behavior within formation rocks, including geo-bio-chemical reactions prone to occur during the UHS process. The analysis of H2 thermo-physical suggests a more operational challenge than storing CO2 or natural gas. Commercial demonstration of UHS is crucial, while all the ongoing field tests of UHS (pure H2) worldwide are still in their early stages. Regionally, deep salt caverns and saline aquifers with closed structures or regional seals provide the best structural traps for UHS due to their tight and secure seal system. Down-dip aquifers and sedimentary packages in the eastern basins at the Arabian platform are more attractive and safer options. The discussed analysis of UHS potential in Saudi Arabia sheds light on its integration possibility into the circular carbon economy (CCE) framework to achieve a net-zero emission by 2060.