Assisted Cement Log Interpretation

Time, Eirik; Viggen, Erlend Magnus; Mishra, Siddharth; Berg, Eirik

doi:10.2118/209529-ms

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The quality of the old cement sheaths may also be tested again to ensure that they are still effective. To date, pressure testing, cement bond logging, and variable density logging can evaluate the quality of the cement sheath [3]. However, pressure tests may not be economically feasible, and field experience has shown that, in some cases, pressure tests may cause damage to the cement sheath [4], whereas cement bond logging (CBL) and variable density logging (VDL) will not.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

et al. 2022

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The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.

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Section: Introductionmentioning

confidence: 99%

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

et al. 2022

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Annuli Material Pseudo-Density Estimation from Standard Integrity Logging Data

Merciu

2023

SPE/IADC International Drilling Conference and Exhibition

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Bonding to casing material characterization is a topic of interest in the context of well barrier evaluation. There is a consolidated need directed to log data interpreters to answer questions related to the type of material present in the casing annuli of a given well. The objective of this work is to explore the possibility to provide a complementary answer to current general acoustic impedance and attenuation practice. We reiterate that knowledge about the density of the annuli material can reduce the well barrier evaluation uncertainty. We are describing and exemplifying a rapid method to estimate the "pseudo – density" of the material in the annuli at the time of data acquisition. We define "pseudo-density" as the ratio between estimated impedance (from ultrasonic pulse-echo) and annuli apparent velocities (from ultrasonic oblique incidence techniques). Resulting "pseudo-density" maps are constrained with prior information from geology, logging, and drilling data. This allows the interpreter to judge the uncertainty in the current interpretation practice and contextualization of the annuli material into the borehole reality. The examples provided show that when the underlying physics principles are met, the results are clear and a robust interpretation at scale along the entire logged path is possible. The estimated complementary results are affected by inherent errors and uncertainty associated with acoustic impedance and velocity estimation. However, using prior knowledge information, intervals with high uncertainty (such as unusually high impedances or large errors in the velocity estimation) are flagged providing the interpreter with information background easy to be used for final delivery. Along with standard applications, the proposed method includes the basic mandatory tool for evaluating, validating, and incorporating the forecasted growth based on nuclear integrity logging behind the casing evaluation techniques.

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Improve Well Integrity Using an Annular Barrier AI tool

Time

Berg

Mishra

2023

SPE/IADC International Drilling Conference and Exhibition

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The Assisted Cement Log Machine Learning (ML) tool – or Annular barrier AI tool - developed by Equinor - is being used to interpret cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process. (Reference to SPE paper: Assisted Cement Interpretation project). Annular conditions are usually separated into High, Medium and Low probability for hydraulic isolation. The internally developed annular condition code descriptions at Equinor are separated into 30 specific classes, which supports more nuanced and objective expert interpretations. The paper will discuss how this framework has positively impacted the performance of the trained ML model. In addition, we report how this tool is being used to speed up and increase consistency in the log interpretation process, and how it can be used to efficiently share expert knowledge when training new professionals into Equinor's Cased Hole Logging Group. Furthermore, the paper will discuss ongoing research to improve the capabilities of this tool, like supporting the use of cement logs from additional service vendors, and how it could be potentially expanded to extract relevant information from historical reports to improve formation bond predictions. The ML model is trained using selected and calculated features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. Training and prediction are done in the cloud and accessible through an Application Programmable Interface (API) which makes it convenient to integrate the tool with any cement log interpretation software. Through the API, the interpretation software uploads a cement log and swiftly receives predictions for the complete log, including hydraulic isolation probabilities and confidence curves, which are used as decision support for the final expert interpretation. The ML model is regularly retrained with an ever-growing data set from real operations performed by Equinor. The uploaded data undergoes an automatic quality assurance before it is used as training data, and the model's performance is evaluated at each retraining. To improve the cement log interpretation consistency in the industry and to ensure that our work can benefit the industry as widely as possible, the results will be made available as open source. This paper will discuss the challenges making such an ML tool open source, and the how the idea of Federated Learning could be used to share this solution in the industry.

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Assisted Cement Log Interpretation

Cited by 4 publications

References 8 publications

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

Annuli Material Pseudo-Density Estimation from Standard Integrity Logging Data

Improve Well Integrity Using an Annular Barrier AI tool

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