Prediction of Ultimate Bearing Capacity of Oil and Gas Wellbore Based on Multi-Modal Data Analysis in the Context of Machine Learning

Introduction

In recent years, with the development of big data and artificial intelligence technology, drilling decision-making has gradually evolved from being experience driven and logic driven to data driven. Therefore, marine development has made great progress in drilling, thereby promoting the overall efficiency of exploration and development. At present, however, the problem of wellbore instability is still outstanding, and especially for some complex blocks, the phenomenon of sticking and falling blocks still occurs frequently during drilling. Fixing this problem requires reaming, circulation, and other operations, and the reaming operation is difficult, resulting in an increase in the nonproduction time and leading to a rise in production costs (Heydari et al., 2022). Pre-drilling prediction and real-time evaluation of wellbore stability during drilling, as well as countermeasures from drilling fluid performance, drilling engineering operation, and other aspects, help reduce drilling complexity and accident risk. These measures help companies meet the important demand for further cost reduction and increased efficiency in oil and gas development.

In the process of oil and gas drilling, energy companies face a prominent problem of wellbore instability, which seriously affects the timeliness of drilling and restricts the improvement of economic benefits of enterprises. The development of technology at home and abroad tends to be technical innovation and digitization. It is an extremely important development direction at present to build a digital technology system that serves the cost reduction and efficiency improvement of oil fields by using massive data (Yang et al., 2022; Pei et al., 2022). If big data analysis (for mechanical parameters) and artificial intelligence technology (neural networks, genetic algorithms, etc.) can be used to fully mine and analyze the aforementioned data, then providing new solutions to complex problems such as wellbore instability during drilling and even making major breakthroughs in some fields is possible (Wang et al, 2022; Yilmaz et al., 2020). Gu et al. (2022) proposed a method of predicting the stability of directional wells while drilling according to the principle of rock mechanics and seismic inversion. Through real-time analysis of leakage logging data by layered modeling of neural networks, the borehole wall stability in front of the drill bit is predicted while drilling by using seismic inversion wave impedance data. Jing et al. (2017) used elastic wave theory to analyze the influence of density, stress, strain, and other parameters on the velocity of vertical and horizontal waves; they proposed that lithology, saturation state, and stress state were key factors. Domestic research on wellbore stability is based on conventional logging, mud logging, and seismic data. Establishing the quantitative relationship between various obtained data and wellbore stability parameters enables various algorithms, mathematical models, and physical models to be used to predict wellbore stability. Among these models the prediction model of wellbore stability is mainly based on the rock mechanics model, although there are a few methods to predict wellbore stability using intelligent algorithms. Predicting rock mechanical properties before drilling enables substituting parameters, such as rock mechanical properties and in-situ stress, into the mechanism model to calculate formation collapse pressure and a formation fracture pressure profile. Setting up these parameters optimizes the machine learning algorithm and establishes an intelligent prediction model of well collapse and lost circulation driven by data and mechanism before drilling, thus enabling a better evaluation result of wellbore stability to be obtained (Jin et al., 2022).

In summary, the prediction of oil and gas well wall state based on machine learning has certain advantages. Machine learning transforms oil and gas drilling prediction from the traditional method based on the mechanism model to the intelligent prediction method integrating mechanism model and data model. In this paper my main contribution is explaining these three processes: