A comprehensive review on Enhanced Oil Recovery by Water Alternating Gas (WAG) injection

[1] The effect of pore fluids on seismic wave attenuation in carbonate rocks is important for interpreting remote sensing observations of carbonate reservoirs undergoing enhanced oil recovery. Here we measure the elastic moduli and attenuation in the laboratory for five carbonate samples with 20% to 30% porosity and permeability between 0.03 and 58.1 mdarcy. Contrary to most observations in sandstones, bulk compressibility losses dominate over shear wave losses for dry samples and samples fully saturated with either liquid butane or brine. This observation holds for four out of five samples at seismic (10–1000 Hz) and ultrasonic frequencies (0.8 MHz) and reservoir pressures. Attenuation modeled from the modulus data using Cole-Cole relations agrees in that the bulk losses are greater than the shear losses. On average, attenuation increases by 250% when brine substitutes a light hydrocarbon in these carbonate rocks. For some of our samples, attenuation is frequency-dependent, but in the typical exploration frequency range (10–100 Hz), attenuation is practically constant for the measured samples.

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Seismic Wave Attenuation in Carbonates

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

Pore-scale X-ray imaging with measurement of relative permeability, capillary pressure and oil recovery in a mixed-wet micro-porous carbonate reservoir rock

Hafez H. Hafez

Papers

Sequence Stratigraphy-Based Geological Modeling of Upper Thamama (Lower Cretaceous) Reservoirs of a Giant Abu Dhabi Oil Field, United Arab Emirates

Capillary Pressure and Resistivity Index Measurements in a Mixed-Wet Carbonate Reservoir

4D Seismic Monitors Fluid Changes Over Time in a Carbonate Reservoir

Sub-miscible Gas-WAG Injection Piloting and Compositional Modeling in a Carbonate Reservoir

A Workflow for Developing Water-Oil Relative Permeability Hysteresis - Super Giant Carbonate Reservoir Case Study