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

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

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

Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs

Simulation models are routinely used as a powerful tool for reservoir management. The underlying static models are the result of integrated efforts that usually includes the latest geophysical, geological and petrophysical measurements and interpretations. As such, these models carry an inherent degree of uncertainty. Typical uncertainty analysis techniques require many realizations and runs of the reservoir simulation model. In this day and age, as reservoir models are getting larger and more complicated, making hundreds or sometimes thousands of simulation runs can put considerable strain on the resources of an asset team, and most of the times are simply impractical. Analysis of these uncertainties and their effects on well performance using a new and efficient technique is the subject of this paper. The analysis has been performed on a giant oil field in the Middle East using a surrogate reservoir model. The surrogate reservoir model that runs and provides results in real-time is developed to mimic the capabilities of a full field simulation model that includes one million grid blocks and takes 10 hours to run using a cluster of twelve 3.2 GHz CPUs. In order to effectively demonstrate the robustness of Surrogate Reservoir Models and their capabilities as tools that can be used for uncertainty analysis, one must demonstrate that SRMs are competent in providing reasonably accurate results for multiple realizations of the reservoir being studied. In order to demonstrate such robustness and their predictive capabilities as well as their limitations, this paper will examine the performance of the surrogate reservoir models on different geologic realizations of the static model.

https://www.pe.wvu.edu/Publications/Pdfs/SPE101474.pdf

Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model

Short-Term and Long-Term Aspects of a Water Injection Strategy

Preliminary results from a time-lapse 4D pilot study shows that saturation changes over time can be monitored in an onshore carbonate reservoir with the 4D surface seismic method. The 4D results were successful in showing that 4D responses can be observed in reservoir zones where sufficient saturation changes had occurred. The results of the 4D pilot showed that 3D seismic surveys can be repeated onshore with acceptable background noise levels, if done in the correct manner. Model predictions were found to be in agreement with the 4D results. 4D responses were found in the main reservoir layer where saturation changes are occurring and not in the water leg where saturation changes have not occurred. The 4D responses are in good agreement with available well control by greater than 80 percent. Detailed 4D validation showed the 4D results to match available production logging tool (PLT) measurements surprisingly well. The results also suggest that pressure changes are contributing to some of the observed 4D responses. Based on the results of this pilot, it has been shown that 4D can monitor saturation changes in time in this carbonate reservoir. The 4D results are helping to assess sweep efficiency and identify potential bypassed oil reserves.

Hafez H. Hafez

Papers

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

Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs

Uncertainty Analysis of a Giant Oil Field in the Middle East Using Surrogate Reservoir Model

Short-Term and Long-Term Aspects of a Water Injection Strategy

Successful Pilot Onshore Abu Dhabi Shows That 4D Can Monitor Fluid Changes In a Giant Middle East Carbonate Field