Identifying reservoir opportunities using automated well selection and ranking

• Published in: World Oil Vol. 241; no. 10; pp. 37 - 41
• Main Authors: Darabi, Hamed, Benhallam, Wassim, Smith, Johanna, Salehi, Amir, Castineira, David, Gringarten, Emmanuel
• Format: Trade Publication Article
• Language: English
• Published: Houston Gulf Publishing Co 01.10.2020
• Subjects: Automation; Candidates; Case studies; Collaboration; Connectivity analysis; Constraint modelling; Drainage; Drainage area; Economic forecasting; Engineering; Engineers; Evaluation; Feasibility studies; Fluid filters; Geoengineering; Geological mapping; Geologists; Geology; Identification; Inspection; Mapping; Mathematical models; Oil; Oil wells; Optimization; Optimization techniques; Probability theory; Reservoirs; Risk assessment; Statistical analysis; Subject specialists; Target recognition; Teams; Water flooding; Wells; Middle East
• ISSN: 0043-8790

METHODOLOGY The framework consists of several components, including: * Engineering analytics - Automatically performs decline curve analysis, using AI-based event detection and type curve generation; allocates production per zone for each well, using a series of datadriven techniques, integrating production, completion, rock and fluid properties, and PLT/ILT information. * Remaining pay identification - Identifies uncontacted pay with many distinguishing features, such as pay connectivity analysis, automatic baffle identification, perforation strategy and standoff constraints. * Drainage analysis - Estimates areas that have been drained by existing wells, by offering both volumetric and facies-based drainage areas. * Wellbore accessibility - Automatically evaluates the mechanical feasibility via AIbased deciphering of wellbore diagrams (automated wellbore diagram digitization and feasibility classification). * Geological risk assessment - Automatically assesses structural and mapping risks. * Production rate forecast - Estimates production gains by selecting from many available options containing statistical, analytical and ML-based models. * Target search - Identifies optimal locations for placing target wells, using robust and fast optimization techniques, featuring relative probability of success mapping and comprehensive geo-engineering constraints. Additionally, petrophysical characterization, geo-model assessment, production forecast, and mechanical feasibility are available to accelerate the vetting process and promote collaboration between asset team members. After screening and evaluating more than 10,000 recompletion and 17,000 vertical well possibilities, dozens of recompletions/reactivations (with potential oil gains between 4,600 to 7,000 bopd) and vertical wells (with potential oil gains between 7,700 to 9,000 bopd) were selected that satisfied the geo-engineering constraints and passed the scrutiny of a team of domain experts. Thousands of contenders for each scenario were examined, and the final collection consisted of 155 feasible options, after applying a series of filters, which included minimum initial rate, minimum oil thickness, maximum acceptable uncertainty, and the inspection of geologists, reservoir engineers, and production engineers.