https://www.pe.wvu.edu/Publications/Pdfs/AIReservoirModeling.pdf

Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM)

History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process timeconsuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate. This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a second. SRM is built using a handful of geological realizations. In this study, a synthetic reservoir model of a heterogeneous oilfield with 24 production wells and 30 years of production history was used as the ground truth (the subject and the goal of the history match). An SRM was created to accurately represent this reservoir model. The history matching process for this field was performed using the SRM and by tuning static data (Permeability). The result of this study demonstrates the capabilities of SRM for fast track and accurate reproduction of the numerical model results. Speed and accuracy make SRM a fast and effective tool for assisted history matching.

https://shahab.pe.wvu.edu/Publications/Pdfs/SPE169507.pdf

Artificial Intelligence (AI) Assisted History Matching

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.

https://www.mdpi.com/2311-5521/4/3/126/pdf

Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

Performance comparison of several response surface surrogate models and ensemble methods for water injection optimization under uncertainty

In this paper a new class of reservoir models that are developed based on the pattern recognition technologies collectively known as Artificial Intelligence and Data Mining (AI&DM) is introduced. The workflows developed based on this new class of reservoir simulation and modeling tools break new ground in modeling fluid flow through porous media by providing a completely new and different angle on reservoir simulation and modeling. The philosophy behind this modeling approach and its major commonalities and differences with numerical and analytical models are explored and two different categories of such models are explained. Details of this technology are presented using examples of most recent applications to several prolific reservoirs in the Middle East and in the Gulf of Mexico. AI-Based reservoir models can be developed for green or brown fields. Since these models are developed based on spatio-temporal databases that are specifically developed for this purpose, they require the existence of a basic numerical reservoir simulator for the green fields while can be developed entirely based on historical data for brown fields. The run-time of AI-Based reservoir models that provide complete field responses is measured in seconds rather than minutes and hours (even for a multi-million grid block reservoir). Therefore, providing means for fast track reservoir analysis and AIassisted history matching are intrinsic characteristics of these models. AI-Based Reservoir Models can completely substitute numerical reservoir simulation models, work side by side but completely independent or be integrated with them in order to increase their productivity. Advantages associated with AI-Based Reservoir Models are short development time, low development cost, fast track analysis and have the practical capability to quantify the uncertainties associated with the static model. AI-Based Reservoir Model includes a novel design tool for comprehensive analysis of the full field and design of field development strategies to meet operational targets. They have open data requirement architecture that can accommodate a wide variety of data from pressure tests to seismic.

https://www.pe.wvu.edu/Publications/Pdfs/SPE143179.pdf

Reservoir Simulation and Modeling Based on Pattern Recognition

Reservoir simulation is the industry standard for reservoir management that is used in all phases of field development. As the main source of information, prediction and decision-making, the Full Field Models (FFM) is regularly updated to include the latest measurements and interpretations. A typical FFM consists of large number of grid blocks and usually takes hours for each run. This makes comprehensive analysis of the solution space and incorporation of the FFM in smart fields impractical. Surrogate Reservoir Models (SRMs) are introduced as a bridge to make Real-Time Reservoir Management possible. SRMs are replicas of FFM that can run in fractions of a second. They accurately mimic the capabilities of FFM and are used for automatic history matching, real-time optimisation, real-time decision-making and quantification of uncertainties. This paper presents the development of SRM using the state of the art Artificial Intelligence and Data Mining (AID Accepted: November 4, 2008]

https://shahab.pe.wvu.edu/Publications/Pdfs/IJOGCT-Feb2009.pdf

Abi Modavi

Papers

Development of Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir