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Record W2063591529 · doi:10.2118/2005-222

Case History: Successful LWD Formation Evaluation and Drilling for Petro-Canada, at Wilson Creek, Alberta

2005· article· en· W2063591529 on OpenAlexaffabout
Sarah H. Williams, T. Grills, Bryan Vandal, Kris Sanford

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsDrillingGeologyPetroleum engineeringMining engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The Mississippian Pekisko Formation is an important reservoir unit in southwest Alberta, running SSE-NNW parallel to the Pekisko erosive edge from about T20 R8W4 to T64 R12W5. The present study involves the area around Wilson Creek and Gilby North (T41-43, R3-5W5; Figure 1). In this area, although the most porous limestone and coarse-grained, dolomitized lithologies have been successfully produced using vertical wells, most production from the tighter, largely undolomitized rocks has been achieved only since the advent of horizontal drilling. Here, some 56 horizontal wells have been drilled since 1997, the majority by Encal/Calpine and Petro- Canada. These have resulted in 46 producers (43 gas and 3 oil) with 12 month initial production (IP) rates up to 376 e3m3/D and cumulative production to date of 0.1 to 17.8 BCF per well (mean 2.2 BCF) 1. All wells drilled prior to the 9–28 HZ were drilled with limited real time logging while drilling information; mainly gamma ray, gas detector and rate-of-penetration data. For the two wells discussed in this paper, a full suite of real time logging tools were run in conjunction with real time internet access to the data, which lead to more efficient execution of the operation. Introduction Asphaltene Precipitation in oil reservoir is Prejudicial to the economy of oil production because its deposition impairs oil flow to the wellbore. In site asphaltenedeposition causes permeability reduction and wettability changes. It is of great interest to be able to predict the condition that lead to such precipitation and to quantify the amount of precipitated asphaltenes. Gradual progress has been made in the modeling of asphaltene precipitation from Petroleum fluids in the last two decades. The modeling approaches can be classifiedinto five different categories:polymer solubility representation,equation of state,colloidal,thermodynamic micellization, andmolecular thermodynamics. The solubility model has been developed by Hirshberg et al(1). 4) and further applied by several authors(2),(3). The reversible liquid-liquid equilibrium (LLE) is governed by activity coefficients that are calculated from the Flory-Huggins polymer solution theory in order to account for the non- ideality of asphaltene and resin molecules. Another group of authors(4),(5) used a similar approach but considered asphaltenes as a solid component. The general equation that relates the solid to the liquid fugacity of pure sphaltenes depends on temperature and fusion properties(6). Recently, G.R. Pazuki and M. Nikookar et. al. (7) presented better results by modification of Flory- Huggins model. The equation of state model has been established by Gupta(8), and later modified by Nghiem et al. (9) It is also referred to as the solid model. The precipitated asphaltene phase is represented by a pure solid component while the liquid and vapor phases are modeled with an equation of state (EOS). The Peng- Robinson EOS was used to calculate the asphaltene precipitation by assigning different values of binary interaction coefficients to the precipitating and nonprecipitating pseudo components with light components. The model is easy to implement, however, it requires tuning of many parameters to match experimental data. Geology The Pekisko is a carbonate sequence up to 30 m thick that was deposited along a shoreline above a ramped continental margin (continuously sloped, as opposed to a rimmed margin marked by reef buildups). The dominant lithologies are coarsegrained limestones (grainstones) containing ooids and algalcoat

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.206
Teacher spread0.188 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2005
Admission routes2
Has abstractyes

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