Optimizing Horizontal-Wellbore and Fracture Spacing With Interactive Reservoir and Fracturing Simulation
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 137416, ’Optimization of Horizontal Wellbore and Fracture Spacing Using an Interactive Combination of Reservoir and Fracturing Simulation,’ by R.S. Taylor, SPE, Halliburton; M.A. Glaser, SPE, J. Kim, SPE, and B. Wilson, Murphy Oil; G. Nikiforuk, V. Noble, and L. Rosenthal, WestMan Exploration; R. Aguilera, SPE, University of Calgary; O. Hoch, SPE, Hoch & Associates; K. Storozhenko, KJS & Associates; and M. Soliman, SPE, N. Riviere, T. Palidwar, SPE, and R. Romanson, SPE, Halliburton, prepared for the 2010 Canadian Unconventional Resources & International Petroleum Conference, Calgary, 19-21 October. The paper has not been peer reviewed. Horizontal drilling with multistage-fracturing technology has made shale-gas reserves available globally. More recently, these technologies have been applied to new and mature oil fields. Key for economic optimization of these assets is determining the fracture spacing to use along a horizontal wellbore. Of equal importance is the spacing to use for multilaterals and the wellbores themselves to achieve optimal drainage of the reservoir. In addition, design of the fracturing treatments must be optimized. A combination of reservoir and fracturing simulation was applied. The required input data are provided through a combination of advanced log and core analyses, diagnostic fracture-injection testing (DFIT), rate-transient analysis (RTA), and characterization of fracture geometry through microseismic monitoring. Fluid rheology is characterized with pressurized rheometers and flow loops. Introduction The goal of this study was to optimize spacing of fractures, multilaterals, and horizontal wellbores by use of a combination of reservoir and fracture simulation for both oil and gas reservoirs. The full-length paper provides detailed examples of an interactive combination of numeric reservoir simulation and hydraulic-fracture simulation. Given the heterogeneity of reservoirs as well as varying economic drivers for different operators, it is critical that specific studies be conducted for each operator, area, reservoir, and depth to make specific recommendations. Combined use of many reservoir-characterization tools can result in recommendations that provide a significant increase in the net present value (NPV) of the assets under several modeled changes. Although many complex methods were used, the quantifiable result was cumulative production vs. time as a function of fracture spacing used in the well design. These data can then be used in economic models to optimize fracture spacing, spacing between wellbores, and multilateral considerations. They can also be used to develop economic sensitivities for changes in well design by quantifying the economic benefit of the change. Operators may use this method to conduct sensitivity analyses on a range of future anticipated commodity prices and well-construction and operating costs to optimize the asset design for current conditions and future markets, or to evaluate the economics of potential new plays.
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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 | 0.001 | 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 | 0.001 |
| 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".