Bibliographic record
Abstract
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 ?-exponent (d_mod) as an applicable source of diagnostic data for the downhole drilling system. Design experiments were conducted to study the response of the d_mod 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 hindcast 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 deepwater 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 oilwell construction costs and other associated systems or applications with similar performance conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".