Abstract Data-driven methods serve as robust tools to turn data into knowledge. Historical generally has not been used in an effective way analyzing processes due lack of a well-organized data, where there is huge potential turning terabytes With the advances and implementation data-driven models have become more widely-used analysis, predictive modeling, control optimization several processes. Yet, industry overall still skeptical on use methods, since they are data-based solutions rather...

Abstract Objective Digital twin technology offers significant opportunities for the utilization and optimization of unconventional gas fields by providing virtual replicas physical assets processes. The idea a digital is to build model completely devoid typical physics equations that analytical or numerical simulation usually depends on. A purely data driven without any context. This paper explores potential benefits applications twins in fields, focusing on enhancing operational efficiency,...

Summary The integration of surveillance data analysis, encompassing wellbore pressure, fluid flow rate, tracer injection, and recovery, is pivotal in deciphering the dynamic behavior wells within a geothermal field. This comprehensive study focuses on interconnectivity between producers, gauged by reciprocal-productivity index (RPI), synergy producers injectors, assessed through capacitance-resistance modeling (CRM). modified-Hall analysis further corroborates performance metrics both...

Abstract This study aims to optimize subsurface hydrogen storage facilitate renewable energy integration by addressing efficiency, safety, and long-term sustainability in various geological formations. A multi-disciplinary approach was used model injection withdrawal different reservoirs, including depleted gas fields aquifers. Advanced simulation tools were applied capture the thermodynamic, geochemical, geomechanically behavior of under varying pressure temperature conditions. Sensitivity...

Abstract This paper explores the application of Nested Fourier Neural Operators (FNOs) to enhance accuracy and computational efficiency CO2 storage modeling in subsurface energy systems, focusing on their ability model complex fluid flow geomechanical interactions. The study employed a hybrid approach that integrates traditional numerical methods with dynamic processes involved storage. FNO framework was trained extensive datasets from field experiments high-fidelity simulations, learning...

_ The rapid development of oil and gas intelligent operations depends on artificial intelligence (AI), automation, data analytics to achieve optimal conditions in operations. Digital-twin technology uses virtual copies physical assets perform complex analyses that improve performance while actively identifying mechanical failures before they materialize. Revolutionary drone robotic technologies transform field by enabling autonomous systems critical inspections hazardous environments. These...

This study analyzes and evaluates the performance of various solvers preconditioners for reservoir simulations CO2 injection long-term storage using model 11B SPE CSP (Society Petroleum Engineers, 11th Comparative Solution Project) MATLAB Reservoir Simulation Toolbox (MRST). The 11 serves as a benchmark testing numerical methods solving partial differential equations (PDEs) in simulations. research focuses on Biconjugate Gradient Stabilized (BiCGSTAB) Loose Generalized Minimum Residual...

Deep learning approaches can be utilized for production forecasting in cases with complex profiles. In this work, a deep algorithm was developed to use the pertinent profiles such as oil rate, GOR, WOR train model. Using reservoir simulation, model first benchmarked using synthetic two cases: (i) decline rate under constant GOR and (ii) variable GOR. The architecture of recurrent neural network structured by one LSTM layer four dense layers. Also, public dataset monthly shale wells published...

Abstract Production forecasting is crucial for field development. Machine Learning models have shown the potential to overcome limitations of conventional methods like Numerical simulations, reduced-order modeling, and decline curve analysis. The paper presents a newly developed machine-learning-assisted rapid production method, involving massive geomodel compression (18000 times) followed by neural-network-based regression. method first compresses large, heterogeneous shale low-dimensional...

Abstract This article explores the application of Generative Adversarial Networks (GANs) in improving subsurface rock analysis. GANs generate realistic images features, enhancing accuracy and efficiency estimating properties. The objective is to advance field analysis, offering insights into potential GAN techniques. reviews recent developments data-driven reconstruction technology, specifically focusing on GANs. It highlights GANs’ ability estimate features like porosity permeability. These...

Latest technological developments and applications made optimal control methods usage in well placement intelligent fields practical beneficial to increase the production. Effective of these strongly depends on detailed evaluation economic view performance reservoirs that have high uncertainty, particularly. There are several optimization ranging from classical reservoir engineering derivative-free hybrid methods. TNO's Olympus model used globally as a benchmark ISAAP-2 Challenge used....

Abstract With improvements in technology and increasing amount of opportunities more challenging assets, the use smart well technologies to improve recovery has caught significant attention oil gas industry last decade. Several workflows have been developed proposed order automate whole process that integrates several subprocesses focusing on specific parts surface or subsurface phenomena. Reservoir sweep is a crucial part efficiency, especially where investment done by means installing...