Assisted History Matching of a Highly Heterogeneous Carbonate Reservoir Using Hydraulic Flow Units and Artificial Neural Networks

Abstract

Abstract Germik, a mature heavy oil field in Southeast Turkey, has been producing for more than 60 years with a significant decline in pressure and oil production. To predict future performance of this reservoir and explore possible enhanced oil recovery (EOR) scenarios for a better pressure maintenance and improved recovery, generation of a representative dynamic model is required. To address this need, an integrated approach is presented herein for characterization, modeling and history matching of the highly heterogeneous, naturally fractured carbonate reservoir spanning a long production history. Hydraulic flow unit (HFU) determination is adopted instead of the lithofacies model, not only to introduce more complexity for representing the variances among flow units, but also to establish a higher correlation between porosity and permeability. By means of artificial intelligence (AI), existing wireline logs are used to delineate HFUs in uncored intervals and wells, which is then distributed to the model through stochastic geostatistical methods. A permeability model is subsequently built based on the spatial distribution of HFUs, and different sets of capillary pressures and relative permeability curves are incorporated for each rock type. The dynamic model is calibrated against the historical production and pressure data through assisted history matching. Uncertain parameters that have the largest impact on the quality of the history match are oil-water contact, aquifer size and strength, horizontal permeability, ratio of vertical to horizontal permeability, capillary pressure and relative permeability curves, which are efficiently and systematically optimized through evolution strategy. Identification and distribution of the hydraulic units complemented with artificial neural networks (ANN) provide a better description of flow zones and a higher confidence permeability model. This reduces uncertainties associated with reservoir characterization and facilitates calibration of the dynamic model. Results obtained from the study show that the history matched simulation model may be used with confidence for testing and optimizing future EOR schemes. This paper brings a novel approach to permeability and HFU determination based on artificial intelligence, which is especially helpful for addressing uncertainties inherent in highly complex, heterogeneous carbonate reservoirs with limited data. The adopted technique facilitates the calibration of the dynamic model and improves the quality of the history match by providing a better reservoir description through flow unit distinction.