Regression analysis and its application to oil and gas exploration: A case study of hydrocarbon loss recovery and porosity prediction, China

Abstract

In oil and gas exploration, elucidating the complex interdependencies among geological variables is paramount. Our study introduces the application of sophisticated regression analysis method at the forefront, aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging. Through a rigorous assessment, we explore the efficacy of eight regression models, bifurcated into linear and nonlinear groups, to accommodate the multifaceted nature of geological datasets. Our linear model suite encompasses the Standard Equation, Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net, each presenting distinct advantages. The Standard Equation serves as a foundational benchmark, whereas Ridge Regression implements penalty terms to counteract overfitting, thus bolstering model robustness in the presence of multicollinearity. The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models, enhancing their interpretability, while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator, offering a harmonized solution to model complexity and comprehensibility. On the nonlinear front, Gradient Descent, Kernel Ridge Regression, Support Vector Regression, and Piecewise Function-Fitting methods introduce innovative approaches. Gradient Descent assures computational efficiency in optimizing solutions, Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns, and Support Vector Regression is proficient in forecasting extremities, pivotal for exploration risk assessment. The Piecewise Function-Fitting approach, tailored for geological data, facilitates adaptable modeling of variable interrelations, accommodating abrupt data trend shifts. Our analysis identifies Ridge Regression, particularly when augmented by Piecewise Function-Fitting, as superior in recouping hydrocarbon losses, and underscoring its utility in resource quantification refinement. Meanwhile, Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A, evidencing its aptness for intricate geological structures. This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector. The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction, evaluation, and recovery.