Successful Field Use of Advanced Dynamic Models

Abstract

Abstract This paper focuses on how advanced dynamic models can be best used in critical drilling and completion operations. To date, R&D in this area has focused mainly on developing reliable and accurate models for drilling and completion operations, and has not focused on how these models are used in practice. Examples from recent managed pressure operations in North Sea wells are presented. These examples give insight into how such models can best be utilized in the future. In these examples dynamic models were used to calculate target surface pressure to keep open hole pressure within narrow margins between fracture and pore pressures. Models were utilized under several displacement operations, including liner cementing, to determine how to regulate choke pressure to compensate for changes in density and rheology. Important factors leading to a successful operation were 1) update of input parameters, 2) model calibration prior to critical operations, and 3) good communication with the drilling crew. Based on the experience from these operations, the paper discusses in detail how to move to the development of future software tools that make advanced dynamic models more accessible for all kinds of operations. Some key points of this vision are: Models are directly linked to real time dataModels are integrated with intelligent algorithms that automatically interpret deviations between measured data and model predictionsModels and diagnosis algorithms are integrated with data acquisition systems and run real time driven by real time data.Models do not require the presence of model experts. This aims at a software system that is useful also for personnel with little knowledge and experience in advanced modeling during drilling operations. This will provide more reliable monitoring of all drilling and completion operations, reliable warning when operations become critical, and help lead the crew through critical phases of the well. This will lead to a reduction in non-productive time through better decision making and improved focus on critical operations.