Tight-Rock RTA: Global Band of Shale Producers Joins Forces To Improve Crucial Diagnostic
Bibliographic record
Abstract
More than three dozen oil and gas producers are working together to solve one of the grand challenges faced by all in the increasingly global unconventional sector. That is the ability to better predict tight-oil and -gas production using rate transient analysis (RTA). Considered an “unconventional diagnostic” when first introduced in the 1970s, RTA relies on fluid rates and flowing pressures to inform engineers on what their reservoirs will ultimately yield. Unfortunately, the low permeability of tight rocks and a myriad of dynamics stemming from hydraulic fracturing have undercut the simplicity of the tool. This has given rise to arguments that RTA is not a fit for unconventional reservoirs. But a joint industry project with 37 operators from around the world is betting against that notion. Those taking part represent the biggest and most active shale plays in the US—a list that includes Apache Corp., BP’s shale unit BPx. Devon Energy, EQT, Hess Corp., and Ovintiv. Others hold assets in Canada’s Montney and Duvernay formations, Argentina’s Vaca Muerta Shale, and the emerging Jafurah tight-gas basin that Saudi Aramco is in the early stages of developing. The organizer behind the joint project is petroleum engineering software and consultancy Whitson. The Trondheim-based firm said the client consortium is likely the largest of its kind to focus squarely on improving RTA for tight reservoirs. In October, the multinational group wrapped up its first phase of study with a set of best practices and the release of new add-ons for Whitson’s software service. Why this might evolve into a notable development is because the deliverables are all designed to help standardize a recently debuted alternative called the numerically enhanced RTA workflow. Introduced by reservoir experts at Houston-based Apache and IHS Markit, this “enhanced” version of numerical RTA has caught the industry’s attention for its ability to account for the effects of multiphase flow in tight wells. The first details about the approach were shared with the industry in 2020 in URTeC 2967. Whitson reports that a newly established workflow for numerical RTA created with operator clients delivers consistent well analysis in seconds to a few minutes, and importantly, was proven to work across their disparate geologies. Mathias Carlsen is a general manager at Whitson and has been at the center of the joint industry project from the beginning. Here, he helps explain what the industry should know about the RTA joint project and where it is heading. Assembling a Trifecta Carlsen, a reservoir engineering expert, described the overarching goal of the joint project not as a mission to discover a panacea for RTA but one designed to fill in its big gaps with some recent innovations. “What we’ve been successful at through the joint industry project is getting the new tools ready so that the workflows can be easily used and in standardizing them for a wide range of wells found in every single unconventional basin in the world,” he said.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".