In this paper, an integrated procedure was adopted to obtain accurate lithofacies classification to be incorporated with well log interpretations for a precise core permeability modeling. Probabilistic neural networks (PNNs) were employed to model lithofacies sequences as a function of well logging data in order to predict discrete lithofacies distribution at missing intervals. Then, the generalized boosted regression model (GBM) was used as to build a nonlinear relationship between core permeability, well logging data, and lithofacies. The well log interpretations that were considered for lithofacies classification and permeability modeling are neutron porosity, shale volume, and water saturation as a function of depth; however, the measured discrete lithofacies types are sand, shaly sand, and shale. Accurate lithofacies classification was achieved by the PNN as the total percent correct of the predicted discrete lithofacies was 95.81%. In GBM results, root-mean-square prediction error and adjusted R-square have incredible positive values, as there was an excellent matching between the measured and predicted core permeability. Additionally, the GBM model led to overcome the multicollinearity that was available between one pair of the predictors. The efficiency of boosted regression was demonstrated by the prediction matching of core permeability in comparison with the conventional multiple linear regression (MLR). GBM led to much more accurate permeability prediction than the MLR.

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The gas-assisted gravity drainage (GAGD) process has been suggested to improve oil recovery in both secondary and tertiary stages through immiscible and miscible injection modes. In contrast to continuous gas injection and water-alternating gas, the GAGD process takes advantage of the natural segregation of reservoir fluids to provide gravity-stable oil displacement and improve oil recovery. In the GAGD process, the gas is injected through vertical wells at the top of the reservoir to formulate a gas cap that allows oil and water to drain downward to the reservoir bottom, where horizontal producer(s) are placed. Extensive experimental works and limited reservoir-scale evaluation studies have been conducted to test the effectiveness of the GAGD process performance. In this paper, a comprehensive literature review is presented to summarize all of the references about concepts, principles, and field-scale evaluations of the GAGD process. Particularly, this paper presents an introduction to the mechanisms of CO2–rock–fluid interactions, gas enhanced oil recovery injection approaches, the GAGD process physical model, the factors influencing the GAGD process, and a review of all of the previous field-scale evaluation studies. Furthermore, the validation of the GAGD process in reservoir-scale applications is fully discussed by focusing on its weaknesses with respect to the optimal implementation design for achieving maximum oil recovery.

Unlike the Continuous Gas Injection (CGI) and Water-Alternative Gas (WAG), the Gas-Assisted Gravity Drainage (GAGD) process takes advantage of the natural segregation of reservoir fluids to provide gravity-stable oil displacement and improve oil recovery. In the GAGD process, the gas is injected through vertical wells to formulate a gas cap to allow oil and water drain down to the horizontal producer (s). Therefore, the GAGD process was implemented through immiscible and miscible injection modes to improve oil recovery in a sector of the main pay/upper sandstone member in the South Rumaila oil field, located in Iraq. A high-resolution Multiple-Point Geostatistics-based reservoir characterization was reconstructed to model the lithofacies and petrophysical properties in order to provide the most realistic geological environment for the GAGD process simulation. After upscaling the geostatistical models, EOS-compositional reservoir simulation was built to evaluate the GAGD process and test its effectiveness to improve oil recovery. Next, a notable history matching was obtained with respect to the oil rates, cumulative production, water injection rate, and cumulative injection for the entire field and all the wells. Then, 20 vertical injectors and 11 horizontal producers were installed for CO2 injection and oil production, respectively. The reservoir has 12 layers and the gas injection wells are placed at the crest of the reservoir through the first two top layers to formulate a gas cap. The horizontal production wells were installed in the fifth, sixth, seventh, and eighth layer of high oil saturation. The 2nd, third, and fourth layers were left as transition zones to achieve the gravity drainage. The four bottom layers were left with no injection/production activates as they fully flooded with water. The base case of immiscible CO2-GAGD flooding with default settings of operational well decision factors was adopted for 10 years of future performance prediction. In addition, two other special cases for immiscible and miscible were implemented for 25 years of future performance prediction. The recovery factor given the remaining oil is approximately 7.6% through the primary production by the end of the prediction period. However, the immiscible base case of the GAGD process resulted in reaching recovery factor of 15% given the remaining oil. Additionally, the obtained amount of oil in 10 years primary production can be obtained in only one year by the GAGD base case. On the other hand, the recovery factor through immiscible and miscible special cases of 25 years prediction reached to 30% and 42% given the remaining oil, respectively. A fourth special GAGD process was compared to the Continuous Gas Injection (CGI) and Water-Alternative-Gas (WAG) processes on the same reservoir with similar well constraints. Given the remaining oil, the recovery factor by the end of prediction period is 23.72% through the GAGD process. However, the CGI and WAG have resulted in obtaining 12.35% and 11.37% recovery factors, respectively. Consequently, the feasibility of GAGD process to improve oil recovery was attained by obtaining higher recovery factors than CGI and WAG flooding methods.

Unlike these Continuous Gas Injection (CGI) and Water-Alternating-Gas (WAG) injection modes, the Gas-Assisted Gravity Drainage (GAGD) process takes advantage of the natural segregation of reservoir fluids to provide gravity-stable oil displacement. Specifically, the gas is injected through vertical wells to formulate a gas cap to allow oil and water drain down to the horizontal producer (s) and that would lead to improving oil recovery. Therefore, the GAGD process was implemented through immiscible injection modes to improve oil recovery in a sector of the main pay/upper sandstone member in the South Rumaila oil field, located in Iraq. Design of Experiments (DoE) and Proxy Modeling were adopted to obtain the optimal future oil recovery through the GAGD process. The CO2-GAGD process feasibility was investigated for the immiscible injection mode through the EOS-compositional reservoir simulation with Design of Experiments and Proxy Modeling to obtain the optimal future performance scenario. After conducting the acceptable history matching, the Latin Hypercube Sampling (LHS) was employed as a low-discrepancy and more uniform DoE approach to create hundreds of simulation runs (experiments) in order to construct a proxy-based optimization approach. More specifically, the proxy model represents a metamodel used to evaluate the various designed experiments in the optimization procedure rather than the simulator itself. Then, the second-order polynomial equation was iteratively constructed and validated based on the least mismatch between the oil response calculated by the proxy model and by the simulator. The optimization process searches for the optimal future oil recovery by optimizing the levels of the operational decision factors, which constrain the production and injection activities. These decision factors include maximum oil production, minimum BHP, maximum water cut, and skin factor in the production wells in addition to the maximum gas injection rate and maximum injection pressure in the injection wells. The cumulative oil production was handled as the response parameter that is initially calculated by the compositional reservoir simulation for 10 years of future prediction. The optimal cumulative oil production, by the end of the prediction period, led to obtaining 4.6039 MMMSTB of oil production, while the base case of the GAGD process evaluation of default parameters setting resulted to obtain 4.3887 MMMSTB of oil production. Therefore, the current optimization approach has led to increasing the oil recovery by 215.2 million STB in 10 years of future prediction. The polynomial proxy model was re-validated in a different procedure in comparison with three more proxy models: Multivariate Additive Regression Splines, Fuzzy Logic-Genetic Algorithm, and Generalized Boosted Modeling. The validation procedure integrates cross-validation with Root Mean Square Error to find the optimal proxy model that can be considered as a perfect metamodel for the nonlinear CO2-EOR flooding through the GAGD process. For the least mismatch obtained between the simulator- and proxy-based cumulative oil production, each of GBM and FUzzy-GEnetic can be adopted as an accurate simplified alternative metamodel to the full resolution compositional reservoir simulator through the GAGD Process evaluation and prediction.

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