Pre-Drilling Prediction of 3D Geomechanical Parameters Based on Seismic Data: A Case Study of Tarim Oilfield
Bibliographic record
Abstract
ABSTRACT A pre-drill prediction method of 3D geomechanical parameters based on seismic data is proposed. Firstly, the wave impedance parameters are predicted in the target area by the logging-constrained method, which mainly uses post-stack seismic data and logged data from drilled wells. The density and velocity data are obtained by separating the wave impedance data. Then, the density and velocity data are used as input to calculate 3D geomechanical parameters in the region, including elasticity parameters, strength parameters, and stress parameters. In particular, experimental data are used to correct the accuracy of the model. The results accurately reflect the geological complexity and non-homogeneity of the region by evaluating the elastic properties, mechanical properties, and stress magnitude of each point. This method can greatly improve the longitudinal resolution of the inversion results by fully exploiting a priori information from the logs and involving them in the seismic inversion process. Pre-drill parameters prediction of a complex field in the Tyuritag of the Tarim Basin is carried out. INTRODUCTION A growing number of oil and gas field development projects are facing the challenge of safe, rapid, and efficient development, such as offshore projects like Hibernia and the Gulf of Mexico in Canada, and onshore projects in tectonically active areas like the Cusiana field in Colombia and the Tarim Basin in China. However, as drilling depths continue to deepen, the geological environment encountered in oil and gas development is becoming increasingly complex. The difficulty of engineering problems related to geomechanics is also increasing. On the one hand, there are more and more complex accidents in various wells, such as well wall instability, well leakage, and sand production. Underground accidents seriously increase the time and cost of construction operations. It is estimated that at least 10% of the average well budget is used for unplanned operations due to wellbore instability (Sheng, 2006; Wei, 2012). On the other hand, the inaccuracy of geomechanical modeling makes geomechanics-related engineering measures unable to achieve the expected goals. In shale oil and gas development, about 30-50% of fracturing clusters do not contribute to product improvement. The root cause is poorly designed hydraulic fracturing strategies due to the lack of accurate geomechanical data (Zhang, 2018; Parshall, 2015).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".