A Method for Predicting Formation Pore Pressure in Carbonate Rocks

Author:

Tao Zhenyu1,Liu Yuhan2,Ye Yuguang2,Fan Honghai1,Sun Lewang1,Shang Heya3,Wang Lihao4

Affiliation:

1. China University of Petroleum, Beijing, China

2. CNPC Engineering Technology Research and Development Company Limited, Beijing, China

3. Hebei Normal University of Science & Technology, Qinhuangdao, Hebei Province, China

4. Tianjin Expressway Group Co., Ltd., Tianjin, China

Abstract

Abstract Formation pore pressure refers to the pressure of the pore and the fluid in it, which is an important parameter in drilling engineering, related to well depth structure design, drilling fluid preparation, and casing matching. Accurately predicting formation pore pressure helps to reduce drilling accidents and achieve safe drilling. Due to the complexity of the causes of abnormal pressure in carbonate rock formations, it is difficult to predict the formation pore pressure. Taking Block M in Qaidam Basin of Qinghai Oilfield in China as an example, this paper proposes a prediction method for formation pore pressure in carbonate rock formations based on analyzing the causes of abnormal pressure. Firstly, based on core analysis and well-log interpretation, this paper analyzes the causes of abnormal pressure in Block M; According to the causes of abnormal pressure, the Fan comprehensive interpretation method is selected as the calculation method for the formation pore pressure. The model is modified based on well-log data and experimental data, and the BP neural network is used to correct the prediction error. Based on this model, the formation pore pressure of two wells is predicted. Compared with the measured points, the error is less than 5%, which meets the engineering requirements.

Publisher

SPE

Reference22 articles.

1. State-of-the-art in artificial neural network applications: A survey[J];Abiodun;Heliyon,2018

2. Drilling Manual[M];Drilling Manual Writing Group,1990

3. The formation pore pressure Analysis Carbonate Strata in Shunnan[D];Dai,2017

4. Pore Pressure Prediction by Empirical and Machine Learning Methods Using Conventional and Drilling Logs in Carbonate Rocks[J];Delavar;Rock Mechanics and Rock Engineering,2022

5. The Effect of Overburden Stress on Geopressure Prediction from Well Logs[J];Eaton;Journal of Petroleum Technology,1972

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