Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 163988, ’Practical Insights and Benefits of Integrating Technology Into Exploration, Appraisal, and Development of Unconventional Gas and Liquid- Rich Shale Reservoirs,’ by Bora Oz, SPE, David Braun, SPE, Sanjay Vitthal, SPE, Viannet Okouma, SPE, Mathieu Molenaar, SPE, Chandran Peringod, SPE, Sergei Kazakoff, SPE, Yongyi Li, Michele Asgar-Deen, and David Langille, SPE, Shell Canada, prepared for the 2013 SPE Middle East Unconventional Gas Conference and Exhibition, Muscat, Oman, 28-30 January. The paper has not been peer reviewed. In the current high-oil-/low-gas-price North American environment, and considering the new options available for well-completions technology in unconventional reservoirs, recent industry activities have turned their focus to the areas of liquid-rich shale (LRS) and light tight oil (LTO) along with unconventional tight and shale gas. Integrated workflows are important to the successful execution of this portfolio, and cutting-edge integrated technologies must be viewed as key enablers. Introduction Commercially and economically viable shale plays require the presence of some key conditions, which fall into two groups: reservoir quality/productivity and fracability (i.e., the ability to place effective and conductive stimulations). Factors that affect reservoir quality and productivity are the matrix porosity and permeability, organic carbon content, maturity and kerogen type, and fluid composition and pressure/volume/temperature properties. Two key factors that affect fracability are mineralogy and local in-situ-stress magnitude, orientation, and distribution. To develop unconventional gas (UG), LTO, and LRS plays more effectively, significant advancements in understanding geological, geophysical, geochemical, geomechanical, petrophysical, reservoir, and stimulation properties have been made in the last decade. However, we are still just scratching the surface, and rapid integration across disciplines is required for success. In addition, continuous learning from exploration through to the end of field life must occur. Geophysical Practices and Technologies Geophysical technologies have always played a crucial role in the exploration and development of unconventional resources. They are used to define subsurface properties and to detect and monitor microseismic activities in the subsurface during completion and production. The recent, rapid increase in unconventional appraisal activity and the large diversity of geological settings require continuous innovation in geophysical-data collection, processing, and interpretation. Engineering activities, such as well and completion design, hydraulic-fracture monitoring, and production analysis and forecasting, are the major drivers for geophysical technologies. The three most common geophysical technologies are surface seismic, nonseismic methods (mainly potential-field methods), and microseismic or other downhole acoustic techniques (Fig. 1). For exploration and appraisal, a large number of 2D-seismic lines, with limited 3D-seismic surveys, are required data sets. In the development stage, 3D-seismic data are crucial for reservoir characterization and well placement (as well as engineering-related applications). Advanced interpretations have become an essential part of understanding play complexity, optimizing well placement, and mitigating drilling geohazards. Seismic data also play an important role in providing geological boundaries for building geological frameworks and geomechanical models.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 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".