Holistic Appraisal Strategy Aims To Get the Most Out of Unconventional Reservoirs
Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 162776, ’Appraisal Excellence in Unconventional Reservoirs,’ by M.K. Burkholder, SPE, E.M. Coopersmith, SPE, and J.H. Schulze, SPE, Decision Frameworks, prepared for the 2012 SPE Canadian Unconventional Resources Conference, Calgary, 30 October-1 November. The paper has not been peer reviewed. This paper presents a pragmatic approach to frame, evaluate, and compare appraisal strategies in resource plays. The unique approach starts with framing the overall field-appraisal plan, holistically, and then progresses to evaluating the most commonly encountered individual appraisal decisions, enabling teams to answer the following questions: What are the key uncertainties, and which appraisal options should we consider to resolve them? Is there value in running a pilot to determine the optimal well spacing, and, if so, what is the best pilot design? Do we want to run a completions pilot, and how should we configure it? Do we need to pilot where and how to orient our laterals? Can we justify seismic to high-grade our drilling program? Introduction Open, Structured, Auditable Decision Process. Decision analysis is designed in two key phases, framing and analysis. Framing involves defining the decision problem, setting the objectives and decision criteria, agreeing on the alternatives to be evaluated, and structuring the evaluation. The analysis involves quantifying the key uncertainties and their effect on the development, estimating the uncertainty-reduction potential for each of the appraisal options, and applying standard value-of-information (VOI) analysis practices to gain insight on the strategic direction. Both framing and analysis are important and necessary. Frame and Evaluate the Development Strategy Without New Information Understanding the Risk Profile. Appraisal strategies are information strategies and, as such, require a VOI workflow to evaluate them. To define the value that information adds to an asset, the team must first understand the value of the asset without additional information. Therefore, VOI=(asset value with information)−(asset value without information). The evaluation results generated in this phase set the baseline “asset value without information” that excludes collecting any new information other than what one would normally collect in the course of a development. A cumulative probability plot displays the risk profile of a development—the chance of having a positive economic development—and is generated using probabilistic analysis methods. An example cumulative probability plot is displayed in Fig. 1. Three possible zero net-present-value (NPV) points (A, B, and C) are mapped on the plot. If the NPV zero point of the asset development is near C, there is a very low chance of achieving positive economic results (approximately 7%). In this situation, there is likely little value in appraisal because there is such an extremely low probability of finding an economic development. The reverse is true if the NPV zero point is near A. There is such a high chance of finding an economic development (approximately 91%) that there may also not be any value in appraisal. The team instead might consider stepping directly into development. When the NPV zero point is near B, there is considerable upside potential and downside risk of development and, as such, there is likely to be higher value in appraisal.
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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 | 0.001 | 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".