Well Portfolio Optimisation: Accelerating Generation of Well Intervention Candidates with Automated Analytics and Machine Learning - A Case Study from Attaka Field, Indonesia
Authors: Siahaan Edwin , Mamat Irwan , Laksana Senna Sun , Setyowibowo Agung , Naufal Aulia Ahmad , Wulandari Octy Edriana , Metra Sabrina , Nainggolan Ardi Karta , Arinandy Okky Idelian , Ellen Livia , Pramita Maharani Devira , Tjiong Agnes , Wilian Yus , Songchitruksa Praprut 
Published: October 2022

Advancements in technology, complemented with the abundance of static and historical data brought AI and digital automation adapted very well into the oil and gas industry. Specially to solve the challenges by the engineers in selecting well intervention candidates. In Attaka Field, a multi-layered offshore field in Indonesia, workover and well service (WOWS) have been one of the strategies to reduce production decline. With traditional workflows that absorb data from multiple unconsolidated sources and data format and resource limitation, reviewing 400+ wells that penetrates more than 200 reservoirs may take 2-3 months process with a reduced scope of review. As an addition, not all data and values are justified for the prioritization process. An intelligent automated solution termed as WEPON was developed to improve decision speed and quality in Attaka Field WOWS candidate screening.

WEPON was built on top of a data science platform to ease the development, production and maintenance of the analytics engine and its data pipeline. More than 15 data sources, ranging from reservoir properties, allocated production data, up to well schematics were consumed and aggregated in this solution's flow. The main components for WEPON includes: 1. Technical analysis with analytics and ML plus multi-criteria decision-making process to identify high potential completions, both produced and virgin ones 2. Adopting from the field's old workflow, feasibility checks to surface and subsurface constraints for the proposed completions 3. Diagnosing the wells and determine the right workover/ intervention opportunities 4. Calculating each well's subsurface and surface risks, and historical success rate to be integrated with the well's NPV to produce its expected value (EV) 5. Running on-demand economic analysis accessible from the solution's UI, the engine is tied into the operator's economic analysis tool that contains the currently used calculation and scheme 6. A presentation of the results on a web-based application.

As the main process is triggered to be run on a weekly basis, the automation of WEPON helps to increase Attaka Field review size to the whole fields, as well as reducing 89.7% of time from 3 days to review a well to hours of run to review the whole field, enabling engineers to spend more time on high-cognitive components of the existing workflows. Moreover, it has shifted the approach to a more data-driven one leading up to smarter decisions. The implementation of this WEPON is the pilot in the Indonesian National Oil Company, PERTAMINA. This is also the first time the solution developed on a data science platform, allowing the tool to be evergreen and extensible process. This implementation is also the first one to integrate an economic analysis tool through its API.