Optimizing Oil and Gas Acquisitions Using Recommender Systems

TL;DR

This paper applies a Factorization Machine recommender system to oil and gas well acquisition data, improving recommendation accuracy and providing insights into company-well interactions to optimize purchase decisions.

Contribution

It introduces a novel application of Factorization Machines for well acquisition recommendations, incorporating company and well features to enhance decision-making.

Findings

Model achieved a hit rate of 0.680

Reciprocal rank of 0.469 indicates reasonable ranking quality

Precision of 0.229 and recall of 0.463 demonstrate moderate recommendation effectiveness

Abstract

Well acquisition in the oil and gas industry can often be a hit or miss process, with a poor purchase resulting in substantial loss. Recommender systems suggest items (wells) that users (companies) are likely to buy based on past activity, and applying this system to well acquisition can increase company profits. While traditional recommender systems are impactful enough on their own, they are not optimized. This is because they ignore many of the complexities involved in human decision-making, and frequently make subpar recommendations. Using a preexisting Python implementation of a Factorization Machine results in more accurate recommendations based on a user-level ranking system. We train a Factorization Machine model on oil and gas well data that includes features such as elevation, total depth, and location. The model produces recommendations by using similarities between companies and wells, as well as their interactions. Our model has a hit rate of 0.680, reciprocal rank of 0.469, precision of 0.229, and recall of 0.463. These metrics imply that while our model is able to recommend the correct wells in a general sense, it does not match exact wells to companies via relevance. To improve the model's accuracy, future models should incorporate additional features such as the well's production data and ownership duration as these features will produce more accurate recommendations.