An Innovative Approach for Pore Pressure Prediction and Drilling Optimization in an Abnormally Subpressured Basin
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Bibliographic record
Abstract
Abstract Up to now, 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 5 wells drilled in a township that covers the study area. Pore Pressure was calculated from Sonic Logs and the Modified D Exponent using Eaton’s Method (Eaton, 1975), which proved to be the most effective approach for abnormal subpressured conditions over a variety of methods tested (Contreras et al., 2011). The optimization procedure was carried out using the Apparent Rock Strength Log (ARSL), which is an effective indicator of formation-drillability and 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. Especial 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 rollercone bit IADC 547 with at least 0.73 horsepower in the bit per square inch provides the best quality of 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 7 bit runs while keeping the cuttings quality, bit wear 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it