Enhancing Breakdown Pressure Predictions in Ultra-Tight Formations Through Robust Machine Learning Techniques

Abstract

ABSTRACT Accurately estimating rock breakdown pressure is crucial for designing effective hydraulic fracturing operations, particularly in unconventional ultra-tight reservoirs where hydrocarbon extraction is challenging. However, conducting experimental studies on hydraulic fracturing is both time-consuming and costly. To address this, we employed robust machine learning (ML) tools to estimate the breakdown pressure. The research comprised two stages: an extensive experimental phase followed by the development of ML prediction models using the obtained data. The ML models were trained using experimental factors such as injection rate, confining stress, fluid viscosity, and rock characteristics, including unconfined compressive strength, Poisson's ratio, tensile strength, porosity, permeability, and bulk density. Six machine learning techniques—K-Nearest Neighbor (KNN), Random Forest (RF), Decision Trees (DT), artificial neural networks (ANN), gradient boosting (GB), and adaptive gradient boosting (Adaboost)—were employed to construct the prediction models. With the optimal settings for the ML models, the breakdown pressure of the tight formations was accurately predicted with a 99% accuracy. The proposed ML approaches not only offer significant cost savings but also serve as a quick evaluation tool to assess the development prospects of tight rocks. INTRODUCTION Low porosity and permeability are typically characteristics of unconventional tight reservoirs. The most cost-effective method of extracting resources from these reservoirs is to inject high-pressure fluids into engineered channels or fissures known as fractures. Hydraulic fracturing treatment is the name of the entire industrial procedure (Agency, 2016; Teufel and Clark, 1984; van Eekelen, 1982; Wang et al., 2018). The pressure at which rock cracks during a hydraulic fracturing procedure is known as the breakdown pressure. It is the highest pressure ever logged during the injection of wellbore's fracturing fluid injection (Detournay and Carbonell, 1997; Patel et al., 2017; Song et al., 2019; Warpinski et al., 2004). In order to extend the economic life of a well or field, the fracturing procedure might be done numerous times. Several mixtures of fracturing fluids may be employed to increase the effectiveness of the fracturing process depending on the kind of formation and its properties. In order to fracture unconventional reservoirs, common fracturing fluids include polymers, water, methanol, oil, linear gel, or a mixture of methanol and water (Gomaa et al., 2014a; Wu et al., 2019). Many analytical, computational, and experimental research has been performed to predict and understand the fracture breakdown pressure (Barree et al., 2009; B. Dusseault et al., 2014; Goodarzi et al., 2015; Gutierrez and Lewis, 1998; Haifeng et al., 2013; Schmitt and Zoback, 1993; Warpinski et al., 1979).