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Record W2063778491 · doi:10.2523/iptc-13346-ms

Staged design of an EOR pilot

2009· article· en· W2063778491 on OpenAlexaboutno aff
Bhargaw Adibhatla, Robert Chick Wattenbarger

Bibliographic record

VenueInternational Petroleum Technology Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersExxon Mobil Corporation
KeywordsProcess (computing)Computer scienceScheduleEnhanced oil recoveryLead (geology)Risk analysis (engineering)Systems engineeringPilot plantEngineeringPetroleum engineering

Abstract

fetched live from OpenAlex

Abstract Due to the complexity and uncertainty associated with most enhanced oil recovery (EOR) processes, a small-scale pilot is often needed to demonstrate the successful application of an EOR process within a specific reservoir prior to wider commercial implementation. To help manage the complexity and competing cost, schedule, and technical priorities of a pilot, a systematic approach to planning and designing a pilot has been developed. The approach, which is described in this paper, covers various subsurface activities necessary for design of an EOR pilot. Issues related to facilities and other aspects critical to pilot success are also addressed, but in less detail. The sequence of these activities is described and managed in defined stages. The relationship between various activities within a given stage is described using an activity matrix. The activity matrix has proven to be a useful tool for planning and prioritizing various pilot activities. Examples of specific items in the staged approach to EOR pilot design are provided. Introduction A well-designed pilot can be a key element in the successful commercial application of an EOR process. A poorly designed pilot can be costly and lead to an incorrect commercial decision, long delays, or a failed implementation. To help manage the complexity and challenges associated with EOR pilots, guidelines were developed that describe various recommended activities for pilot design. The guidelines, which are summarized in this paper, are designed to be fairly broad and describe recommended activities that are applicable to most EOR pilots (e.g., thermal, gas, chemical). As such, the guidelines serve as a starting point for project-specific guidelines that should be customized for the specific process, field, and pilot business needs. The staged process for pilot design reflects experiences from ExxonMobil's own studies and applications of EOR pilots as well as the published experiences of others. ExxonMobil has piloted several EOR processes throughout the last 40 years. Some examples include chemical processes at the Loudon field1–7 in the United States, the Pembina field8 in Canada, the West Yellow Creek field9 in the United States, steamflood and LASER applications10–12, steam-foam applications13–14, CO2 flooding at the Means field in the United States15, Solid Stabilized Emulsions (SSE)16 in Canada, piloting of miscible gas injection at Judy Creek17–18 in Canada, and others19–21. The material presented in this paper builds upon the lessons learned from these pilots. This paper is not intended to describe the overall process for evaluating and implementing an EOR process, nor is it meant to summarize best practices for EOR pilots. These are described elsewhere22–23. Rather, the approach to pilot design described in this paper is meant to serve as an overall guide to planning and properly sequencing the activities associated with EOR pilot design. These activities include the cross-functional interaction between reservoir engineers, facilities engineers, surveillance engineers, geoscientists, and other disciplines expected in any field development activity. As much as possible, the guidelines focus on the activities that are specific to EOR pilots, leveraging as much as possible existing project management procedures and best practices. EOR Staged Evaluation and Development Process Pilot design and implementation is part of a broader workflow for evaluating and implementing an EOR process for a particular field. The overall process, which has been described in earlier publications22–23, is briefly summarized in the next section. An overview of a staged process to evaluate and implement EOR processes for a specific field is shown in Figure 1.22 EOR evaluation starts with initial data collection, identification of potential EOR recovery processes, and screening economics. After initial screening, promising EOR processes are evaluated in greater detail through laboratory experiments and detailed modeling studies. If the results of the in-depth analysis indicate economic benefit, a field pilot may be performed to address key uncertainties. The decision to pilot, requirements of a successful pilot, types of field pilots, and other piloting best practices have been described elsewhere22. If the pilot is successful and the EOR process remains economically attractive, then the pilot is followed by commercial application of the EOR process on a wider scale.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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