Abstract
Having a robust field development plan (FDP) for mid-size mature oil fields generally poses considerable challenges in the context of the integrational elements of production forecast, operational environment, projects and surface facilities. An integrated FDP combined with data analytics and artificial intelligence (AI) has been introduced and deployed in a heavily compartmentalized offshore field of Turkmenistan.
An integrated approach through data-centric analytics and AI has been proposed for an optimal FDP. It consists of four aspects: model integration, time-series forecast (TSF) of production, AI-assisted operation-schedule generation, and evaluation and selection of scenarios. Firstly, model integration is performed as bringing together both multi-discipline raw data from field measurement and their interpretations that change non-linearly. Secondly, model integration aids in the application of AI for production forecast. A unique AI technique was built to allow raw data and interpretation. Illustratively, the model is capable of forecasting decline curves matching the history production. Meanwhile, engineers’ production forecast inheriting from simulation, machine learning or type curves is also constructed by understanding how/why human-driven forecasts differ from the measured decline and incorporating those insights. In addition, AI-assisted scheduler efficiently allocates resources for operational activities, considering the well planning nature, intrinsic operation properties, project planning process, surface facilities and expenditures. Resources are thus utilized for optimal schedules. Finally, evaluation and selection of FDP scenarios take place by considering the multidimensional matrix of factors. Multiple scenarios are generated and scored, reacting to the change of factors. AI-powered optimization is availed to recommend the most efficient tradeoffs between production and carbon generation.
The implementation of the integrated FDP approach has been successfully applied for the generation of production profiles and operation schedules, which reduces the time by 80% and increasing accuracy by 55%. Production forecast for existing wells and future wells proved to be reliable. It achieved the production targets with proper allocation of schedules, by considering multi-discipline constraints. Through AI-assisted scheduler, different types of rigs were properly assigned to the planned wells, which requires additional rigs based on the outcome. The model was agile to the change and sensitivities of wells requirement, projects uncertainties and cost changes. The optimum FDP scenario was recommended for the business decision, operation guide and execution.
This approach represents a novel and innovative means of integrating and optimizing FDP considering complex factors using AI methods. It is efficient in merging raw data and interpretations for model integration. It accommodates changes and uncertainties from multiple aspects and efficiently generates optimum FDP in a few days rather than months for giant fields. It is the first robust tool that unites subsurface properties, reservoir engineering, production, drilling, projects, engineering and finance for the corporate FDP.
