Matrix Refinement in Mass Transport Across Fracture-Matrix Interface: Application to Improved Oil Recovery in Fractured Reservoirs

In this paper, we will show that it is highly beneficial to model dual-porosity reservoirs using matrix refinement (similar to the multiple interacting continua, MINC, of Preuss, 1985) for water displacing oil. Two practical situations are considered. The first is the effect of matrix refinement on the unsteady-state pressure solution, and the second situation is modeling water-oil, Buckley-Leverett (BL) displacement in waterflooding a fracture-dominated flow domain. The usefulness of matrix refinement will be illustrated using a three-node refinement of individual matrix blocks. Furthermore, this model was modified to account for matrix block size variability within each grid cell (in other words, statistical distribution of matrix size within each grid cell) using a discrete matrix-block-size distribution function. The paper will include two mathematical models, one unsteady-state pressure solution of the pressure diffusivity equation for use in rate transient analysis, and a second model, the Buckley-Leverett model to track saturation changes both in the reservoir fractures and within individual matrix blocks. To illustrate the effect of matrix heterogeneity on modeling results, we used three matrix bock sizes within each computation grid and one level of grid refinement for the individual matrix blocks. A critical issue in dual-porosity modeling is that much of the fluid interactions occur at the fracture-matrix interface. Therefore, refining the matrix block helps capture a more accurate transport of the fluid in-and-out of the matrix blocks. Our numerical results indicate that the none-refined matrix models provide only a poor approximation to saturation distribution within individual matrices. In other words, the saturation distribution is numerically dispersed; that is, no matrix refinement causes unwarranted large numerical dispersion in saturation distribution. Furthermore, matrix block size-distribution is more representative of fractured reservoirs.

ROCK TYPING AND NOVEL APPROACH FOR FLUID-SATURATION DISTRIBUTION IN TILTED WATER/OIL CONTACT RESERVOIRS

Rock typing in carbonate reservoirs has always represented a difficult challenge due to rock heterogeneity. When interpreting electrical logs, the thick carbonate formation can leave an impression of a homogenous environment; however, looking at core analysis and mercury injection capillary pressure (MICP) data, reservoir heterogeneity can be determined. This complexity of the formation characterization presents challenges in reservoirs that contain tilted water/oil contact (WOC). Tilted WOC discovers hydrocarbon saturation below the free-water level, and different events during geological time can contribute to this specific fluid accumulation. Knowledge of the fluid distribution is needed to understand the mechanisms of oil entrapment, oil volumetrics, and potential recovery mechanisms involved in reservoirs under this wettability and WOC conditions. This case study will describe the workflow used to characterize and model an atypical regime like non-water wet formations in reservoirs with tilted WOC. In this study, a combination of electrical logs, core analysis (lithofacies, poro-perm, MICP), and customized workflow was used to characterize, classify, and map facies. Capillary pressure information and formation tester data were integrated and compiled for each facies. Moving forward, a new method was developed to model saturation height functions representing non-water wet formations and tilted WOC phenomena. Fluid and saturation properties are estimated and assigned to each reservoir point and after reservoir rock types (RRT) were defined. This method has been validated by applying the new approach to actual well data. The drainage capillary pressure (Pc) lab data in the reservoir intervals with established conventional WOC complemented interpretation results derived from acquired logs; however, for the reservoirs zones with identified tilted WOC, correlation and matching Pc lab data with logs was not possible. The new method provides saturation properties in formations with complex fluid-rock interactions and phenomena. This work introduces a novel approach to estimate saturation height functions and saturation distribution for reservoirs with complex fluid-rock interaction and distribution, such as non-water wet formations in tilted WOC conditions.

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.