An Innovative Approach for Pore Pressure Prediction and Drilling Optimization in an Abnormally Subpressured Basin
Bibliographic record
Abstract
Summary Thus far, an indirect generalized method to predict pore pressure under subpressured conditions has not been reported in the literature. In this work, an innovative procedure is presented for estimation of pore pressure and optimization of wells drilled in the abnormally subpressured Deep Basin of the Western Canada Sedimentary Basin (WCSB). The procedure starts with detailed evaluation of five wells drilled in a township that covers the study area. Pore pressure was calculated from sonic logs and the modified D exponent by the use of Eaton's method (Eaton 1975), which proved to be the most effective approach for abnormally subpressured conditions over a variety of methods tested (Contreras et al. 2011). The optimization procedure was carried out by use of the apparent-rock-strength log (ARSL), which is an effective indicator of formation drillability and is very sensitive to the pore pressure. Next, optimization of individual sections in each well was carried out to determine the optimum types of bits and operational parameters for the lowest cost of drilling. An artificial-intelligence function was implemented to set up the optimum combination of parameters in such a way that the rate of penetration (ROP) (m/h) was increased after a number of simulation runs while controlling the bit wear. Special attention was focused on tight gas reservoirs for selection of the most suitable parameters that increase the quality of drill cuttings. It was concluded that the roller-cone bit IADC 547 (with at least 0.73 hp in the bit per square inch) provides the best-quality cuttings for the Nikanassin Group. This is of paramount importance for increasing accuracy in the quantitative determination of permeability and porosity from cuttings particularly in those tight gas reservoirs where the amount of cores is very limited. It is concluded that wells in the Deep Basin of the WCSB can be drilled efficiently with seven bit runs while maintaining the cuttings quality, bit-wear level, and well stability at a significantly high average ROP of 13 m/h. Another conclusion is that the normal trend methods from sonic logs are the most effective approach when dealing with an abnormally subpressured basin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".