Surface Drilling Data Can Help Optimize Fracture Treatment in Real Time
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 189810, “Real-Time Completion Optimization of Fracture Treatment Using Commonly Available Surface Drilling and Fracking Data,” by Mohit Paryani, Djamel Sia, Bhavina Mistry, Drew Fairchild, and Ahmed Ouenes, FracGeo, prepared for the 2018 SPE Canada Unconventional Resources Conference, Calgary, 13–14 March. The paper has not been peer reviewed. The objective of optimizing a fracture design is to spend the least amount of money and get the most productivity out of the reservoir by stimulating and contacting as much reservoir rock as possible. This paper presents a unique work flow that addresses in real time the challenges of perforation and fracture-treatment design while accounting for the lithologic and stress variability along the wellbore and its surroundings. Surface Drilling Data in Fracturing Design and Analysis Existing fracturing-design tools often make simplistic assumptions because of a lack of input data. These designs still use layer-cake models and sometimes rely on the data of a nearby well rather than data measured at the considered well. As cost-cutting efforts accelerate in unconventional wells, expecting a log at every well will not be feasible. Because the outcome from the fracturing design heavily depends on specific geomechanical properties, stresses, and surrounding natural fractures, changing the current industry practice of using a nearby well and assuming all subsurface properties to be the same for all stages is imperative. The software used by the authors is able to derive a 3D distribution of the rock properties required as input in the fracturing design. This fracturing-design input available at any well is made possible by using surface drilling data to compute geomechanical logs, pore pressure, stresses, porosity, and natural fractures. Transforming Drilling Data Into Log Properties. The surface drilling data commonly available on any rig includes weight on bit, rate of penetration, rotational speed, and torque. Transforming this data into valuable input for fracturing design starts by computing the corrected mechanical specific energy (CMSE), which removes frictional pressure losses along the drillstring to obtain accurate estimations at the bit. Confined compressive strength (CCS) is then estimated. When the CMSE and the CCS are estimated correctly, the drilling efficiency is computed. Deviations from expected pore pressures are recognized by comparing a hydrostatic trendline with the drilling-efficiency data. The methodology to derive pore pressure from drilling data is based on the concept that the energy spent at the bit to remove a unit volume of the rock is a function of the differential pressure to which the rock is subjected while drilling. The differential pressure provides useful information for deriving the pore pressure. Feeding the rock-strength and pore-pressure information to the real-time geomechanical model helps identify the differential stress variability along the wellbore. Other rock mechanical properties also can be estimated using various lithologies derived from rock-strength information.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".