A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells

Abstract

The study of acid fracture conductivity stands as a pivotal aspect of petroleum engineering, offering a well-established technique to amplify production rates in carbonate reservoirs. This research delves into the intricate dynamics influencing the conductivity of acid fractures, particularly under varying closure stresses and in diverse rock formations. The conductivity of acid fractures is intricately interconnected with the dissolution of rock, etching patterns on fracture surfaces, rock strength, and closure stress. To accurately predict fracture conductivity under different closure stresses, a robust model is necessary. This model involves assessing both the baseline fracture conductivity under zero closure stress and the rate of conductivity variation as closure stress fluctuates. Key among the influential factors affecting fracture conductivity is the type of rock within the reservoir. Understanding and predicting the behavior of different formations under disparate closure stresses poses a significant challenge, as does deciphering the diverse effects of treatment parameters such as acid injection rate and strength on fracture conductivity. In this study, the predictive power of XGBoost, a machine learning algorithm, was explored in assessing acid fracture conductivity in dolomite and limestone formations. The findings revealed XGBoost's ability to outperform previous studies in predicting fracture conductivity in both types of formations. Notably, it exhibited superior accuracy in forecasting fracture conductivity under varying treatment conditions, underscoring its robustness and versatility. The research underscores the pivotal role of closure stress, dissolution rate of rock (DREC), and rock strength in influencing fracture conductivity. By integrating these parameters into the design of acid fracturing operations, accurate predictions can be achieved, allowing for the optimization of treatment designs. This study illuminates the potential of XGBoost in optimizing acid fracturing treatments, ultimately bolstering well productivity in carbonate reservoirs. Furthermore, it advocates for the essential nature of separate modeling and analysis based on rock types to comprehend and optimize fracturing processes. The comparison between dolomite and limestone formations unveiled distinct conductivity behaviors, underlining the significance of tailored analyses based on rock type for precise operational optimization.

Machine Learning in Reservoir Engineering: A Review

“…Consequently, feature selection is a customary practice in these domains. The prevalent feature selection methodologies employed in these fields are described as follows [21,55].…”

“…However, few of them elucidate a process of data preprocessing tailored to specific situations. Researchers predominantly concentrate on aspects like the dataset's source and the division of training and validation sets when discussing data preprocessing [20,50,55], yet there is a noticeable absence of guidance on handling missing data, data cleansing, annotation, and similar procedures. Data preprocessing for specific situations is often crucial for the success of petroleum engineering.…”

“…In the fields of hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection in reservoir engineering, the application of machine learning can be summarized as numerical prediction. The application of machine learning in these scenarios mainly involves the following steps [17,36,55].…”