Research on Recognition Method of Fracturing Dessert of Algaeolite Reservoir in Qaidam Basin

Zheng, Honglin (China University of Petroleum (Beijing)) | Ma, Xinfang (China University of Petroleum (Beijing)) | Zhang, Shicheng (China University of Petroleum (Beijing)) | Liu, Yong (PetroChina Qinghai Oilfield Company) | Guo, Delong (PetroChina Qinghai Oilfield Company) | Lin, Hai (PetroChina Qinghai Oilfield Company)


Abstract Fengxi structure in Qaidam basin is a low porosity and ultra-low permeability reservoir mainly composed of algal limestone on the land of China. Algal limestone is thin and distributed dispersedly. There are problems such as inaccurate dessert identification and large difference in single well production during fracturing. In this paper, reservoir analysis is carried out through cutting electron microscope scanning experiment and core test experiment to obtain and correct various parameters characterizing geological and engineering desserts. A prediction model of algal limestone reservoir fracturing desserts is established by combining the advantages and disadvantages solution distance method (TOPSIS) with the analytic hierarchy process (AHP), and dessert point grades are classified. According to the calculation results, Fengxi Block is generally at the level of Class III and IV desserts, and the artificial fractures are mainly multi fractures near the well and single fractures. Compared with adjacent wells, this method reduces the construction pressure by 10.1% and increases the production by 49.7% after well selection and formation selection, realizing the purpose of precise stimulation of reservoir desserts, and providing a new idea for efficient development of algal limestone reservoirs in Qaidam Basin. Optimization of Dessert Attribute Parameters There are many influencing parameters involved in the identification of fracturing desserts, including geological attributes and engineering attributes (Huang J.L, 2012). In order to accurately screen out the attribute parameters representing dessert, the scoring method is adopted to quantify the score of each attribute one by one, and the attribute parameter with the highest score is selected as the basis for dessert recognition. Attribute scoring methods include accuracy scoring, representation range scoring, and necessity scoring (Han al,2019). In the accuracy score, the accuracy of the parameters obtained by the direct measurement method is high, and the score is 3 points; The accuracy of parameters obtained through inversion of relevant data is medium, and the score is 2 points; The score of other data with poor accuracy is 1 point. In the score of characterization range, the parameter that can represent the continuous well section is scored as 2 points, and the parameter that can only represent the specific depth is scored as 1 point. In the necessity score, the parameter with high importance is scored as 2 points, and the parameter with low importance is scored as 1 point.

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