Stimulation Influence on Production in the Haynesville Shale: A Playwide Examination
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 148940, ’Stimulation Influence on Production in the Haynesville Shale: A Playwide Examination of Fracture-Treatment Variables That Affect Production,’ by Neil Modeland, SPE, Dan Buller, and King Kwee Chong, SPE, Halliburton, prepared for the 2011 Canadian Unconventional Resources Conference, Calgary, 15-17 November. The paper has not been peer reviewed. In most developing shale plays, there is a learning curve for determining which factors affect well production positively and are economical. Evaluating the reservoir on a playwide basis can expedite the ability to determine the optimal treatment method. By use of public production data with a database of stimulation treatments for several hundred wells in the Haynesville shale, variables such as pumping rate, volumes, proppant (type and volume), fracture-stage designs, base fluid, and surfactant use can be analyzed to determine which ones produce a positive effect on well production. Introduction The Haynesville-shale formation of eastern Texas and northern Louisiana is no exception to this learning-curve development process. By combining a database of completion variables from 359 wells completed after 1 January 2009 with public production data, a multioperator playwide sample was obtained to study variations in completion strategy and consequent effects on production. This study investigated only wells that were completed through 1 October 2010, and the well-sample subset constitutes approximately one-fourth of the Haynesville-shale horizontal wells completed during this time interval. Assumptions When associating only completion techniques to the actual production, several assumptions were made that will not always hold true. Though all of the wells in the analysis are Haynesville-shale producing laterals, underlying inequalities are introduced by several differentiators. Wellbore placement, lateral length, and overall well construction differ from well to well. The flowback methodology remains a potential influence on overall production that is not constant across the Haynesville-shale play. Several operators have suggested that flowing back wells less aggressively has increased the estimated ultimate recoveries. Also, when working with monthly production data from a public-domain source, excessive shut-ins of the wells might not be distinguishable in the data set and could portray wells as being lower quality than they really are. Therefore, certain apparent trends could be prone to masking when variations in well construction and flowback parameters are disregarded for this analysis. Production normalization was required in some instances to account for variations such as number of stages or perforation clusters to adequately single out a certain completion variable as the variable of focus.
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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 | 0.001 | 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 | 0.000 |
| 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".