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Record W2142037782 · doi:10.2118/90159-pa

Assessment of Mud-Filtrate-Invasion Effects on Borehole Acoustic Logs and Radial Profiling of Formation Elastic Properties

2006· article· en· W2142037782 on OpenAlexaff
Shihong Chi, Carlos Torres‐Verdín, Jianghui Wu, Faruk O. Alpak

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsBoreholeGeologyPetrophysicsWellboreDrilling fluidMineralogyDrillingMechanicsGeotechnical engineeringPetroleum engineeringMaterials sciencePorosityPhysics

Abstract

fetched live from OpenAlex

Summary Despite continued improvements in acoustic-logging technology, sonic logs processed with industry-standard methods often remain affected by formation damage and mud-filtrate invasion. Quantitative understanding of the process of mud-filtrate invasion is necessary to identity and assess biases in the standard estimates of in-situ compressional- and shear-wave (P- and S-wave) velocities. We describe a systematic approach to quantify the effects of mud-filtrate invasion on borehole acoustic logs and introduce a new algorithm to estimate radial distributions of elastic properties away from the borehole wall. Radial saturation distributions of mud filtrate and connate formation fluids are obtained by simulating the process of mud-filtrate invasion. Subsequently, we calculate radial distributions of the elastic properties using the Biot-Gassmann fluid-substitution model. The calculated radial distributions of formation elastic properties are used to simulate array sonic waveforms. Finally, estimated P- and S-wave velocities for homogeneous, stepwise, and multilayered formation models are compared to quantify mud-filtrate-invasion effects on sonic measurements. We use a nonlinear Gauss-Newton inversion algorithm to estimate radial distributions of formation elastic parameters in the presence of invaded zones using normalized spectral ratios of array waveform data. Inversion examples using synthetic and field data indicate that physically consistent distributions of formation elastic properties can be reconstructed from array waveform data. In turn, radial distributions of formation elastic properties can be used to construct more-realistic near-wellbore petrophysical models for applications in reservoir simulation and production. Introduction During and after drilling, the near-wellbore formation is often altered by stress buildup and release, mud-filtrate invasion, chemical reactions, and many other factors. These alterations cause the physical properties in the near-wellbore region to be different from those of the virgin rock formation. Stress concentration around a wellbore may cause near-wellbore damage and induce formation anisotropy on P- and S-wave velocities. The stress-induced anisotropy can be identified by dispersion analysis (Plona et al. 2002). Positive radial velocity gradients focus the elastic waves propagating away from the wellbore back toward the borehole wall. Such a phenomenon can be identified easily from high-amplitude acoustic arrivals. In this paper, we focus our attention on mud-filtrate-invasion effects only. It is well known that formation properties inferred from wireline logging measurements may not reflect true properties of virgin formations. A realistic description of the invaded zone is important for the processing and interpretation of sonic logs. A common model used in the open literature assumes that a sharp interface exists between the altered zone and the undisturbed formation (Baker 1984). The term "stepwise" is used to describe this type of mud-filtrate-invasion model. Linear-gradient models have been described for syntheses of acoustic waveforms as well (Stephen et al. 1985). Actual radial distributions of elastic wave properties resulting from invasion can be complex and are dependent on the specific petrophysical properties of the rock as well as on the static and dynamic properties of the fluids involved. We divide the invaded zone into a set of concentric radial layers to represent an actual invasion profile and call it a "multilayered" model. Subsequently, we describe a procedure for calculating the radial distributions of formation elastic properties with the Biot-Gassmann fluid-substitution model starting from numerically simulated radial distributions of flow saturation.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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 designObservational
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

Citations17
Published2006
Admission routes1
Has abstractyes

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