Enhancing Well, Reservoir and Facilities Management WRFM Opportunity Identification with Data Driven Techniques

Abstract

A key to successful Well, Reservoir and Facilities Management (WRFM) is to have an up-to-date opportunity funnel. In large mature fields, WRFM opportunity identification is heavily dependent on effective exploitation of measured & interpreted data. This paper presents a suite of data driven workflows, collectively called WRFM Opportunity Finder (WOF), that generates ranked list of opportunities across the WRFM opportunity spectrum. The WOF was developed for a mature waterflooded asset with over 500 active wells and over 30 years of production history. The first step included data collection and cleanup using python routines and its integration into an interactive visualization dashboard. The WOF used this data to generate ranked list of following opportunity types: (a) Bean-up/bean-down candidates (b) Watershut-off candidates (c) Add-perf candidates (d) PLT/ILT data gathering candidates, and (e) well stimulation candidates. The WOF algorithms, implemented using python, largely comprised of rule-based workflows with occasional use of machine learning in intermediate steps. In a large mature asset, field/reservoir/well reviews are typically conducted area by area or reservoir by reservoir and is therefore a slow process. It is challenging to have an updated holistic overview of opportunities across the field which can allow prioritization of optimal opportunities. Though the opportunity screening logic may be linked to clear physics-based rules, its maturation is often difficult as it requires processing and integration of large volumes of multi-disciplinary data through laborious manual review processes. The WOF addressed these issues by leveraging data processing algorithms that gathered data directly from databases and applied customized data processing routines. This led to reduction in data preparation and integration time by 90%. The WOF used workflows linked to petroleum engineering principles to arrive at ranked lists of opportunities with a potential to add 1-2% increment in oil production. The integrated visualization dashboard allowed quick and transparent validation of the identified opportunities and their ranking basis using a variety of independent checks. The results from WOF will inform a range of business delivery elements such as workover & data gathering plan, exception-based-surveillance and facilities debottlenecking plan. WOF exploits the best of both worlds - physics-based solutions and data driven techniques. It offers transparent logic which are scalable and replicable to a variety of settings and hence has an edge over pure machine learning approaches. The WOF accelerates identification of low capex/no-capex opportunities using existing data. It promotes maximization of returns on already made investments and hence lends resilience to business in the low oil price environment.