Enhancing Drilling and Production Through a Digital Twin Methodology

Abstract

Abstract In recent years digitalization has become one of the fastest growing disciplines in the oil and gas industry. Digital solutions reduce project costs, improve personnel and process safety. However, underestimation of the deployment complexity can also bring severe damage to the project execution, unnecessary delays, and ramp-up costs. To minimize negative effects and smoothen delivery process digital twins provide for oil and gas the opportunity to create a digital representation of the equipment, processes, and people behavior. Drilling is a very capital-intensive part of the oil and gas industry. It usessophisticated heavy machineries and typical rig contains various equipment from different vendors.Moreover, drilling process by itself has a lot of uncertainties and depends on human behaviors. Thismix makes most service and operating companies very cautious in introducing new technologies.Therefore, any solutions that can smoothen delivery process and minimize or even avoid any hitchesduring deployment bring a lot of benefits to the industry. Digital twin methodology is an ideal way toaccelerate new technologies deployment through offline full-scale digital testing. The paper highlights efforts to assist operators and drillingcontractors to create a methodology to optimize usage of their assets and improve their processes, using a combination of Systems Architype model and Systems Development lifecycle (SDLC), both well-established methodologies for systems development. Thefocus is on performance improvement, non-productive time reduction, oil and gas productionmaximization. Through emulating rig equipment, drilling process and personnel behavior it is possible toidentify the most critical parameters which guarantee reaching technical limits. Application of digital twin isan iterative process and typically includes several steps. Starting with setting targets, like negative impactreduction of the drilling process on subsequent production. Following by determination of the optimaldrilling parameters based on either historical or modelled data both from drilling and production. Theseparameters are the basis for the new drilling methodology. All deviations from existing drillingmethodologies and generic programs must be carefully analyzed and implemented. Finally drillingmethodology built using digital twin is ready for implementation in real world. The received data is goingto the next iteration to improve the methodology. This approach allows us to train personnel for field specificsituations without risking expensive mistakes in the real world.