Decision-making method of managed pressure drilling based on real-time calculation and analysis models

Abstract

As petroleum exploration and development extend towards marginal, deep, and unconventional resources, drilling challenges such as narrow density windows and high wellbore control risks become increasingly prominent. Managed pressure drilling (MPD) proves effective in reducing drilling operational difficulties and well control risks in narrow safe-density window formations. However, existing MPD systems have limited capabilities for real-time calculation and analysis in the absence of downhole pressure measurement data, hindering accurate guidance for real-time calculation, analysis, and control decision recommendations in both conventional and precise MPD operations.Therefore, based on on-site engineering logging, real-time data from MPD operations, a branch decision tree was established to automatically determine the operational conditions of MPD. This achieved real-time identification of 11 drilling conditions. Starting from the characteristics of two-phase fluid flow in the annulus, coupling wellbore pressure and formation fluid invasion, a predictive model for annular pressure distribution during MPD was established. This model facilitated the calculation, determination, and recommendation of the pre-control value for wellhead backpressure. Building upon theoretical research, a MPD calculation, analysis, and control decision system was developed. Field testing and validation indicated that the system could simulate and analyze wellbore pressure and wellhead backpressure values, automatically identify drilling conditions and downhole complexities. The average error between the calculated and measured values of the standpipe pressure was 1.88%. The accuracy of the predicted wellhead backpressure values based on safe operating conditions compared to the actual controlled values reached 89.8%. This system achieved real-time calculation, analysis, and control decision-making based on real-time engineering parameters, enhancing the reliability and timeliness of wellhead backpressure control in MPD operations. It effectively guided conventional MPD operations and improved the capabilities and intelligence level of precision MPD operations after process optimization.