Hydraulic Fracture Modeling Workflow and Toolkits for Well Completion Optimization in Unconventionals
Bibliographic record
Abstract
Abstract Unconventional reservoirs produce substantial quantities of oil and gas. These reservoirs are usually characterized by ultra-low matrix permeability. Most unconventional reservoirs are hydraulically fractured in order to establish more effective flow from the reservoir and fracture networks to the wellbores. The success of hydraulic fracture stimulation in horizontal wells has the potential to dramatically change the oil and gas production landscape across the globe and the impacts will endure for decades to come. For a given field development project, the economics are highly dependent completion establishing effective and retained contact with the hydrocarbon bearing rocks. Well and completion design parameters that influence the economic success of the field development include well orientation and landing zone, stage spacing and perforation cluster spacing, fluid volume, viscosity and pumping rate, and proppant volume, size and ramping schedule. Optimization of these design parameters to maximize asset economic value is key to the success of every unconventional asset. To achieve an optimal completion design for an asset, the current industry practice is to conduct a large number of field trials that require high capital investment and long cycle-time, and most importantly, significantly erode the project value. The workflow and toolkits shown in this paper offer a much cheaper and faster alternative approach in which to develop an optimal well completion design for EUR and unit development cost (UDC) improvements. It provides an integrated well placement and completion design optimization process that integrates geomechanics descriptions, formation characterizations, flow dynamics, microseismic event catalogues, hydraulic fracturing monitoring data, well completion and operational parameters in a modeling environment with optimization capability. The model is built upon a 3D geological model with multi-disciplinary inputs including formation properties, in-situ stresses, natural fracture descriptions, and well and completion parameters (i.e., well orientation, landing interval, fluid rate and volume, perforation spacing, and stage spacing). Upon calibrating with the hydraulic fracturing diagnosis data, the model provides optimized well completion design, and guidance on data acquisition and diagnostic needs to achieve EUR performance at optimized costs. Field trials based on recommendations from the approach have yielded encouraging production uplift and have led to a significant reduction in the number of trials and cost compared to the commonly used trial-and-error approach. We believe it is technically feasible to derive an optimal completion design using a subsurface based forward modeling approach which will deliver significant value to the industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".