Pore Pressure Prediction in Unconventional Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Understanding pore pressure prediction in unconventional plays is important for executing a safe drilling strategy and for accurate production modeling. Experience from several unconventional plays highlights key aspects of pore pressure prediction work that are different from conventional exploration settings. In conventional exploration, the most common source of overpressure is disequilibrium compaction, where porosity is preserved in mudrocks as pore fluids take on additional overburden load. Traditional petrophysical methods use resistivity, sonic and density data to measure porosity and associate it with vertical effective stress (VES), which is overburden minus pore pressure. In unconventional plays, secondary pressure mechanisms and uplift require other methods because of two influences on pore pressure:hydrocarbon generation andvariations in burial and uplift history. Both of these situations mean that the relationships between vertical effective stress (VES), velocity, density and resistivity will follow unloading paths, not compaction trends. The unloading paths vary depending on the amount of hydrocarbon generated and the amount of uplift. In organic-rich sections, an additional complication arises because pore pressure cannot be de-convolved from total organic carbon (TOC) and gas effects on shale compressional velocity and resistivity. In conventional settings, fluid gradients and contacts are used to translate measured pressure data from one location to another. In unconventional tight reservoirs, the fluids are not connected and this method will not work. Pressure data must be inferred from drilling event and diagnostic fracture injection test interpretations, and a different way to translate data between locations is required. The majority of pressure data in unconventional reservoirs shows that often, the way to translate pressure information from one location to another in the same tight rocks is to use a constant VES. This method combined with understanding variations in uplift history and hydrocarbon generation has been used to successfully predict pressure ranges in multiple unconventional plays. Introduction Unconventional resources plays in shale and tight rocks have become a substantial resource in North America. They are now rapidly being explored and developed outside the United States and Canada in a trend that will likely continue to grow. To economically develop these plays, wells must be drilled as cost effective as possible. To produce from these plays and forecast production, the mechanical properties of the rocks and their stress conditions need to be understood to best stimulate and complete the wells. Pore pressure prediction is integral to both of these activities.
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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 | 0.001 | 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) | 0.002 | 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 it