Analysis of the Applicability of a Risk Quantitative Evaluation Method to High Temperature-Pressure Drilling Engineering

Xie, Ranhong; Zhang, Xingquan; He, Baolun; Zheng, Ningyu; Xu, Yuqiang

doi:10.32604/fdmp.2023.025454

Cited by 1 publication

(1 citation statement)

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“…Among these issues, wellbore leakage has emerged as one of the most prevalent complexities in recent years. This not only escalates drilling costs and diminishes drilling efficiency but also poses a significant risk of wellbore collapse, surges, and even blowouts, leading to major safety incidents [1][2][3]. At the drilling site, the occurrence of fluid loss is typically determined by monitoring changes in drilling-fluid volume and the flow rate at the wellhead.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Leakage Pressure during a Drilling Process Based on SSA-LSTM

Chen,

He,

Wang

et al. 2023

Processes

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Drilling-fluid loss has always been one of the challenging issues in the field of drilling engineering. This article addresses the limitations of a single fluid-loss pressure mechanism model and the challenges in predicting positive drilling-fluid-loss pressure. By categorizing fluid losses of various types encountered during drilling, different geological formations associated with distinct mechanisms are considered. The actual drilling-fluid density in the wellbore at the time of fluid-loss occurrence is taken as a reference value for calculating the positive drilling-fluid-loss pressure of the already drilled well. Building upon this foundation, a combined model utilizing the Sparrow Search Algorithm (SSA) and Long Short-Term Memory (LSTM) neural network is constructed. This model effectively explores the intricate nonlinear relationship between well logging, logging engineering data, and fluid-loss pressure. By utilizing both data from the already drilled wells and upper formation data from ongoing drilling, precise prediction of positive drilling formation fluid-loss pressure can be achieved. Case studies demonstrate that the approach established in this paper, incorporating upper formation data, reduces the average absolute percentage error of fluid-loss pressure prediction to 2.4% and decreases the root mean square error to 0.0405. Through the synergy of mechanistic models and data-driven techniques, not only has the accuracy of predicting positive drilling formation fluid-loss pressure has been enhanced, but also valuable insights have been provided for preventing and mitigating fluid losses during drilling operations.

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Section: Introductionmentioning

confidence: 99%