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Dynamic Pressure Optimization in Dri...

Erivwo, Ochuko,

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Dynamic Pressure Optimization in Drilling Operations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic Pressure Optimization in Drilling Operations // Ochuko Erivwo.
Author:	Erivwo, Ochuko,
Description:	1 electronic resource (130 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31560984
ISBN:	9798342759533

Dynamic Pressure Optimization in Drilling Operations /
Erivwo, Ochuko,

Dynamic Pressure Optimization in Drilling Operations /Ochuko Erivwo. - 1 electronic resource (130 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

In the drilling of oil wells, the need to accurately manage downhole formation pressure has long been established as critical for safety and economics. This thesis presents the research work conducted to improve the reliability of the pressure interpretations from drilling performance data to support optimal operational decision making.The evolution of differential pressure in the drilling wellbore is investigated using the modified \uD835\uDC51-exponent (\uD835\uDC51\uD835\uDC5A\uD835\uDC5C\uD835\uDC51) as an applicable source of diagnostic data for the downhole drilling system. Design experiments were conducted to study the response of the \uD835\uDC51\uD835\uDC5A\uD835\uDC5C\uD835\uDC51 variable to changes in factor effects and systemic error. The results and analysis from the experiments established the interactions of the two main exogenous factors (differential pressure and lithology) that contribute to the variations in rate of penetration (ROP) and the unique effects of these often coalescing factors are extracted. Subsequently, the differential pressure (∆P) system is modelled as a hidden 3 state continuous time Markov process with the signal process of its evolution identified in the changes in the observable ROP encoded in drilling performance data. The state and observation parameters of the hidden Markov model (HMM) are estimated using the Expectation Maximization (EM) algorithm and we show, for a univariate system with a depth dependent time relationship, that the model parameter updates of the EM algorithm equation have explicit solutions. A Bayesian inference model, to determine the safety threshold of the system and early failure prediction at each sampling epoch, is thereafter proposed and illustrated with a hind cast case example. Finally, we introduce a new problem, the Down Hole Drilling Pressures (DHDP) optimization problem for the drilling process, framed as an accelerating performance system subject to random failure in time. The parameters of the problem are formulated within a semi Markov decision process (SMDP) framework and a modified policy iteration approach is used to provide a first solution to the DHDP optimization problem. The optimization algorithm maximizes the sojourn time in the optimal differential pressure (∆P) state, and we show that the system yields the lowest long run average cost for drilling the wells.The decision model developed can be used to optimize the management of down hole drilling pressures in the wellbore especially in offshore and deep-water wells. The potential to predict the differential pressure state transitions ahead of the bit represents a capability not currently available in the industry and these solutions offer significant opportunity for value creation. They ultimately also open up potential for new frontiers of capabilities in the industry that can deliver substantial savings in oil well construction costs and other associated systems or applications with similar performance conditions.

English

ISBN: 9798342759533Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Drilling performance optimization

Dynamic Pressure Optimization in Drilling Operations /
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500 $a Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
500 $a Advisors: Kwon, Roy; Makis, Viliam Committee members: Sarhangian, Vahid; Naguib, Hani.
502 $b Ph.D. $c University of Toronto (Canada) $d 2024.
520 $a In the drilling of oil wells, the need to accurately manage downhole formation pressure has long been established as critical for safety and economics. This thesis presents the research work conducted to improve the reliability of the pressure interpretations from drilling performance data to support optimal operational decision making.The evolution of differential pressure in the drilling wellbore is investigated using the modified \uD835\uDC51-exponent (\uD835\uDC51\uD835\uDC5A\uD835\uDC5C\uD835\uDC51) as an applicable source of diagnostic data for the downhole drilling system. Design experiments were conducted to study the response of the \uD835\uDC51\uD835\uDC5A\uD835\uDC5C\uD835\uDC51 variable to changes in factor effects and systemic error. The results and analysis from the experiments established the interactions of the two main exogenous factors (differential pressure and lithology) that contribute to the variations in rate of penetration (ROP) and the unique effects of these often coalescing factors are extracted. Subsequently, the differential pressure (∆P) system is modelled as a hidden 3 state continuous time Markov process with the signal process of its evolution identified in the changes in the observable ROP encoded in drilling performance data. The state and observation parameters of the hidden Markov model (HMM) are estimated using the Expectation Maximization (EM) algorithm and we show, for a univariate system with a depth dependent time relationship, that the model parameter updates of the EM algorithm equation have explicit solutions. A Bayesian inference model, to determine the safety threshold of the system and early failure prediction at each sampling epoch, is thereafter proposed and illustrated with a hind cast case example. Finally, we introduce a new problem, the Down Hole Drilling Pressures (DHDP) optimization problem for the drilling process, framed as an accelerating performance system subject to random failure in time. The parameters of the problem are formulated within a semi Markov decision process (SMDP) framework and a modified policy iteration approach is used to provide a first solution to the DHDP optimization problem. The optimization algorithm maximizes the sojourn time in the optimal differential pressure (∆P) state, and we show that the system yields the lowest long run average cost for drilling the wells.The decision model developed can be used to optimize the management of down hole drilling pressures in the wellbore especially in offshore and deep-water wells. The potential to predict the differential pressure state transitions ahead of the bit represents a capability not currently available in the industry and these solutions offer significant opportunity for value creation. They ultimately also open up potential for new frontiers of capabilities in the industry that can deliver substantial savings in oil well construction costs and other associated systems or applications with similar performance conditions.
546 $a English
590 $a School code: 0779
650 4 $a Industrial engineering. $3 526216
650 4 $a Mechanical engineering. $3 649730
650 4 $a Petroleum engineering. $3 566616
653 $a Drilling performance optimization
653 $a Hidden Markov Models
653 $a Pore pressure detection
653 $a Semi Markov Decision Processes
690 $a 0546
690 $a 0548
690 $a 0765
710 2 $a University of Toronto (Canada). $b Mechanical and Industrial Engineering. $e degree granting institution. $3 3819498
720 1 $a Kwon, Roy $e degree supervisor.
720 1 $a Makis, Viliam $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05B.
790 $a 0779
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31560984