A Rigorous Coupled Flow-Geomechanics Semianalytical Approach for Analyzing Early Flowback Data from Multifractured Horizontal Wells with Complex Fracture Geometry
Bibliographic record
Abstract
Abstract In this study, a rigorous coupled flow-geomechanics semianalytical approach is presented to analyze flowback data and forecast production performance in multifractured horizontal wells. Hydraulic fracture characterization using post-stimulation flowback data is of critical importance to the quantification of early-time well performance and for efficient development of unconventional reservoirs. However, conventional reservoir (flow) simulators can be challenging to setup for flowback analysis. Further, flow simulators usually approximate stress-dependence of fracture and reservoir parameters, the former of which is particularly important to capture for both the flowback and forward modeling problem, using porosity and transmissibility multipliers. However, in order to apply this approach, transmissibility multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in performance predictions. The goal of this study is to provide a rigorous, coupled semianalytical workflow for hydraulic fracture characterization from flowback data, that utilizes a 3D coupled flow-geomechanics semi-analytical model as its basis. A 3D semi-analytical coupled flow-geomechanical model is developed to capture the complexities of stress-dependence in order to forecast production performance from multifractured horizontal wells. The model can also be used to derive hydraulic fracture properties from early post-stimulation flowback data. An enhanced fracture region (EFR) conceptual model is applied for approximating complex fracture geometries. The fully-analytical fluid flow and semi-analytical geomechanical models are coupled for both the fracture and reservoir regions. The proposed approach requires simultaneous solutions of the fluid flow model (reservoir simulation) and geomechanics model, the latter capturing the stress and deformation behavior of the fracture and reservoir. Coupling between fluid flow and geomechanics is achieved by updating the pressure and stress-dependent properties through a porosity function (coupling parameter) in the flow model for each region (hydraulic fracture and reservoir) at each iteration step. The coupled flow-geomechanics EFR model is validated with fully-numerical simulation. Fracture properties are estimated by using the proposed inverse model for analyzing flowback (water) data. The new flowback analysis approach is applied to synthetic field data and the results compared with the inputs of the synthetic model. With this model, combined with the semi-analytical coupled flow-geomechanics workflow, a more confident estimate of hydraulic fracture properties is obtained.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".