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Duvernay shale is a world class shale deposit with a total resource of 440 billion barrels oil equivalent in the Western Canada Sedimentary Basin (WCSB). The volatile oil recovery factors achieved from primary production are much lower than those from the gas-condensate window, typically 5–10% of original oil in place (OOIP). The previous study has indicated that huff-n-puff gas injection is one of the most promising enhanced oil recovery (EOR) methods in shale oil reservoirs. In this paper, we built a comprehensive numerical compositional model in combination with the embedded discrete fracture model (EDFM) method to evaluate geological and engineering controls on gas huff-n-puff in Duvernay shale volatile oil reservoirs. Multiple scenarios of compositional simulations of huff-n-puff gas injection for the proposed twelve parameters have been conducted and effects of reservoir, completion and depletion development parameters on huff-n-puff are evaluated. We concluded that fracture conductivity, natural fracture density, period of primary depletion, and natural fracture permeability are the most sensitive parameters for incremental oil recovery from gas huff-n-puff. Low fracture conductivity and a short period of primary depletion could significantly increase the gas usage ratio and result in poor economical efficiency of the gas huff-n-puff process. Sensitivity analysis indicates that due to the increase of the matrix-surface area during gas huff-n-puff process, natural fractures associated with hydraulic fractures are the key controlling factors for gas huff-n-puff in Duvernay shale oil reservoirs. The range for the oil recovery increase over the primary recovery for one gas huff-n-puff cycle (nearly 2300 days of production) in Duvernay shale volatile oil reservoir is between 0.23 and 0.87%. Finally, we proposed screening criteria for gas huff-n-puff potential areas in volatile oil reservoirs from Duvernay shale. This study is highly meaningful and can give valuable reference to practical works conducting the huff-n-puff gas injection in both Duvernay and other shale oil reservoirs. [ABSTRACT FROM AUTHOR]

The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance. [ABSTRACT FROM AUTHOR]