A Data-Driven Workflow for Identifying Optimum Horizontal Subsurface Targets

Abstract

Horizontal well development often increases field production and recovery due to increased reservoir contact, reduced drawdown in the reservoir, and a more efficient drainage pattern. Successful field development requires an evergreen backlog of opportunities that can be pursued, which is extremely challenging and laborious to generate using traditional workflows. Here, we present a data-driven methodology to automatically deliver a feasible, actionable inventory by combining geological knowledge, reservoir performance, production history, completion information, and multi-disciplinary expertise. This technology relies on automated geologic and engineering workflows to identify areas with high relative probability of success (RPOS) and therefore productivity potential. The workflow incorporates multiple configuration and trajectory constraints for placement of the horizontal wells, such as length/azimuth/inclination range, zone-crossing, fault-avoidance, etc. The optimization engine is initialized with an ensemble of initial guesses generated with Latin-Hypercube Sampling (LHS) to ensure all regions of POS distribution in the model are evenly considered. The advanced optimization algorithm identifies potential target locations with 3D pay tracking globally, and the segments are further optimized using an interference analysis that selects the best set of non-interfering targets to maximize production. Advanced AI-based computational algorithms are implemented under numerous physical constraints to identify the best segments that maximize the RPOS. Statistical and machine learning techniques are combined to assess neighborhood performance and geologic risks, along with physics-based analytical and upscaled parametric models to forecast phase-based production and pressure behavior. Finally, a comprehensive vetting and sorting framework is presented to ensure the final set of identified opportunities is feasible for the field development plan, given the operational constraints. This methodology has been successfully applied to a mature field in the Middle East with more than 90 vertical well producers and 50 years of production history to identify horizontal target opportunities. Rapid decline in oil production and a subpar recovery factor were the primary incentives behind switching to horizontal development. The search covered both shorter laterals accessible as a side-track from existing wells to minimize water encroachment, and longer laterals that could be drilled as new wells. After filtering based on geo-engineering attributes and rigorous vetting by domain experts, the final catalog consisted of 32 horizontal targets. After careful consideration, the top five candidates were selected for execution in the short term with an estimated total oil gain of 40,000 STB/D. The introduced AI-based methodology has many advantages over traditional simulation-centric workflows that take months to build and calibrate a model. This framework automates steps typically performed during the selection of horizontal well candidates by applying advanced algorithms and AI/ML to multi-disciplinary datasets. This enables teams to rapidly run and review different scenarios, ultimately leading to better risk management and shorter decision cycles with more than 90% speedup compared to conventional workflows.