Optimizing Automated Well Location Planning through Subsurface Data Integration and Machine Learning
Authors: Kamvan Jirach , Tangsawanich Yuranun , Wutthijuk Penprapa , Chobkitchakarn Somphong , Parimontonsakul Monthep 
Published: February 2025

Abstract
Each year, Geologists and Geophysicists face the challenge of identifying new prospect locations to improve company portfolio. These tasks including initial platform review, well planning processes and annual reserves review require extensive time and incredible efforts. To mitigate the time-consuming activities, together with the idea to introduce and encourage an innovative approach on data sciences to subsurface practitioner’s community, the project team initiated a Proof of Concept (PoC) idea on the automated well planning solution.
This innovative technique aims to assist geologists and geophysicists in identifying promising new well locations within their areas of interest. This identification step relies on geological considerations such as well spacing, geological structures, and subsurface data which are facilitated by machine learning mechanisms. Ultimately, we aim to add reserves, reduce time spent on manual evaluations, and enhance overall work efficiency.
Our method begins with a geological structure map (including faults, structure contours, and grids) from two-way-time structural single surface with existing well locations serving as input data. These datasets serve as the conditions for the machine learning algorithms, which are crafted by our data scientists to generate desired potential new well locations.
Our project team, comprising Subsurface and IT practitioners, has successfully developed the initial PoC for automated well planning solution as a prototype. We anticipate substantial benefits from this initiative, including an increase in reserves/resources (equivalent to approximately 10% increase annually) added to the Gulf of Thailand (GoT) portfolio. Additionally, this tool is expected to streamline geologists' and geophysicists' workflow, leading to reduced working hours and increased efficiency when scouting for future potential prospects.
The prototype identified 2,036 wells from all reserves and resources projects in Chevron Thailand Exploration and Production Ltd. (hereinafter ‘Chevron Thailand’) operating areas within less than 4 hours, representing a tremendous reduction in effort compared to traditional methods. Following the initial screening process (according to time limitation), 26 wells underwent detailed validation. Of these, 10 wells were classified as valid and subsequently added to the Chevron Thailand portfolio, significantly impacting short-term development programs. By continuously improving the tool’s efficiency, the multiple subsurface geological structural layers were integrated into the model to pinpoint more accurate new potential locations. Moreover, the automated well planning and platform optimization PoC based on the current GoT basis of design has been successfully proved.
Furthermore, the automation of reserves estimation (another on-going performance estimation project using machine learning) for these newly added wells will be integrated into the process to complete the full cycle of portfolio optimization. This will mark a significant leap forward in our industry, transitioning from manual processes to a fully automated workflow.