Quick Hydraulic Fracture Property Estimation Through Pressure Falloff Data During Fracturing Operations: A Deep-Shale Case Study from the Southern Sichuan Basin
Bibliographic record
Abstract
Abstract Deep shale gas formations with a burial depth larger than 3500 m contain over 65% of the total shale gas reserves in the Southern Sichuan Basin. However, complex reservoir conditions, such as extensively developed natural fractures or faults and large horizontal principal stress differences, generate significant uncertainties in post-fracturing well performance. Quick estimation of hydraulic fracture properties, such as the fracture surface area and effective half-length, via pressure falloff data, after the main fracturing treatment offers a timely and improved understanding of stimulation efficiency and provides key information for post-frac well performance investigation. In this study, we comprehensively investigate fracture properties of different fractured stages, such as main fracture surface area, secondary fracture surface area, and effective main fracture half-length. Then, we analyze the correlation of these properties, productivity, pressure falloff data, and fracturing treatment parameters via a case study. Here, we employ the basic pressure-falloff-based approach of Liu et al. (2020) and further add the impact fracture tortuosity. First, collect high-quality pressure falloff data and generate the log-log diagnostic plot of pressure drop and the corresponding derivative for each stage. Then, generate the composite G-function plot for each stage and find the d(∆p)/dG value when the first closure of the hydraulic fracture occurs. Next, determine the pressure loss caused by the wellbore and near-wellbore fracture tortuosity and calculate the fracture tortuosity. Finally, calculate the main fracture and secondary fracture properties. Well A, a deep shale gas well in the Southern Sichuan Basin, is selected and analyzed. The effective main fracture half-length of well A ranges from 279 ft to 395 ft, depending on the operating and reservoir conditions. Compared with microseismic data, the average main fracture effective half-length is 54.7% of the observed average SRV half-length. The relative magnitude of pressure loss during the pressure falloff period caused by near-wellbore fracture tortuosity can roughly reflect the complexity of the created fracture system. A new fracture complexity evaluation concept is proposed based on the surface area values of main and secondary fractures. For fractured stages, the total pressure drop is positively correlated with the total fracture surface area of the fracture system and total injected fluid volume. The correlation between fracture surface area and gas productivity is weaker compared with that between fracture surface area and water productivity. Some discrepancies in specific stages are possibly caused by abnormal or poor-quality pressure falloff data. By combining other key information on field treatments, the understanding obtained from fracture surface area estimation helps to define changes in treatment design and enhance well productivity. This integrated approach can also serve as a simple but practical tool for estimating hydraulic fracture properties during offshore fracturing.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".