field development

AIQ, ADNOC, and SLB announced a new software suite that integrates artificial intelligence into reservoir analysis and field development projects.

Machine learning has been shown to have a promising role in oil and gas explorations in recent years. Among the applications, determining a proper location for injection and production wells along with their optimal operating conditions is a complex problem.

The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.

This paper summarizes a collaborative industry study to compare observations between shale-play data sets and basins, develop general insights into parent/child interactions, and provide customized economic optimization recommendations.

In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.

Advanced aerial imagery—be it collected from drone, satellite, or fixed-wing aircraft—is one of the best options available for securing the critical information needed to run things smoother in the oil field.

The offering includes automatic evaluation of project economics and enhances collaboration to cut the time required for field development planning by up to 50%, according to the companies.

This paper describes a novel distributed quasi-Newton derivative-free optimization method for reservoir-performance-optimization problems.

The complete paper builds on existing tools in the literature to quantify the effect of changing well spacing on well productivity for a given completion design, using a new, simple, intuitive empirical equation.

This paper describes a development plan for an oil field discovered in a remote offshore environment in the Niger Delta that uses a probabilistic approach to estimate the STOOIP using low, mid, and high cases.