Integrated Simulation for Hydraulic Fracturing, Productivity Prediction, and Optimization in Tight Conglomerate Reservoirs
Bibliographic record
Abstract
With the development of advanced horizontal drilling and multistage hydraulic fracturing techniques, the productivity of unconventional tight oil such as that found in the Baikouquan Formation of the Mahu Sag in the Junger Basin has been increasing. The Mahu oilfield is the largest tight conglomerate reservoir in the world. However, predicting the productivity of fractured horizontal wells in tight oil remains a challenge due to the high heterogeneity of the reservoir and complexity of its hydraulic fractures. Therefore, this study proposes a workflow for coupled geological modeling, along with fracturing and reservoir simulations in tight conglomerate reservoirs. Taking well block Ma131 as an example, a three-dimensional geological model is first built according to the results of geophysical research. The unconventional fracture modeling is used next to establish a fracturing simulation. Given the lack of natural fractures and the high concentration of conglomerate in the study area, the method of the multiple groups of microscale natural fractures is used to simulate the impact of gravel on fracture propagation. The feasibility of the method is verified with microseismic monitoring data and treating curves. Thereafter, an unstructured grid and the INTERSECT reservoir simulator are used to simulate the production performance of horizontal wells. The stress sensitivity effect of the rock matrix and fractures and the changes in the formation flow field caused by fracturing are considered in the reservoir simulation. Hydraulic fractures are found to be unable to form complex fracture networks easily under the conditions of high stress difference, high gravel content, and undeveloped natural fractures. However, they show the characteristics of macroscopic simplicity and local branching. Reducing cluster spacing can increase the complexity of hydraulic fractures and the productivity of horizontal wells. Finally, the proposed integrated simulation process is used to optimize well spacing, fracturing stimulation parameters, and the production system. Overall, the study serves as a guide for maximizing the production and economic benefits of tight conglomerate reservoirs.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".