Study on Real-time Calculation and Control Decision Methods for Managed Pressure Drilling
As petroleum exploration and development expand into marginal,deep,complex areas and unconven-tional resources,drilling challenges such as narrow density windows and high well control risks become increas-ingly prominent.For formations with narrow density windows,managed pressure drilling(MPD)can effective-ly reduce drilling difficulty and well control risks.However,the existing MPD systems have limited computa-tional analysis capabilities without downhole pressure measurement data,making it impossible to accurately guide real-time calculation,analysis,and control decision-making for conventional and fine-tuned MPD opera-tions.Therefore,based on field engineering logging and real-time MPD data,an automatic judgment branch de-cision tree for MPD operations was established,enabling real-time identification of 11 drilling conditions.Start-ing from the characteristics of annular liquid-solid two-phase flow,coupling wellbore pressure and formation flu-id invasion volume,an annular pressure distribution prediction model during the MPD process was established,achieving the calculation,determination,and recommendation of wellhead backpressure pre-control values.On the basis of theoretical research,an MPD calculation analysis and control decision system was developed.Field tests show that during MPD operations,the system can simulate and analyze wellbore pressure and wellhead backpressure values,automatically identify drilling conditions and downhole complexities.The average error be-tween the system's calculated values and the measured standpipe pressure values is 1.88%,and the conformity rate between the predicted and actual wellhead backpressure values under safe operating conditions is 89.8%.This realizes real-time MPD online calculation,analysis,and control decision-making based on engineering parameters,improving the reliability and timeliness of wellhead backpressure control in MPD operations,effectively guiding conventional MPD operations,and enhancing the capability and intelligence level of MPD operations through process optimization.
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