Formation Drillability-Definition, Quantification and Contributions to Bit 
Performance Evaluation

Mensa-Wilmot, Graham; Calhoun, Bill; Perrin, Van

doi:10.2523/57558-ms

Cited by 5 publications

(1 citation statement)

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“…In addition, the nature of the cuttings for each formation is a key parameter for adjusting standpipe pressure (SPP) to control hole cleaning. All of the aforementioned parameters (in addition to the formation type) play a main role in controlling rate of penetration (ROP) [16,17]. Therefore, these drilling parameters are somehow related to the nature of the drilled formation and, in turn, its density.…”

Section: Introductionmentioning

confidence: 99%

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

et al. 2020

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Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.

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Section: Introductionmentioning

confidence: 99%

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

et al. 2020

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Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

Ahmed

Elkatatny

Alsaihati

2021

Computational Intelligence and Neuroscience

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The prediction of continued profile for static Poisson’s ratio is quite expensive and requires huge experimental works, and the discontinuity in the measurement and the limited applicability and accuracy of the present empirical correlations necessitated the utilization of artificial intelligence with its prosperous application in oil and gas industry. This work aims to construct different artificial intelligence models for predicting static Poisson’s ratio of complex lithology at real time during drilling. The functional networks (FN) and random forest (RF) approaches were utilized using the mechanical drilling parameters as inputs. This study uses a vertical well with 1775 records from complex lithology containing shale, sand, and carbonate for model building. Besides, a different dataset from another well was used to check the models’ validity. The results demonstrated that both FN- and RF-based models predicted static Poisson’s ratio with significant matching accuracy. The FN technique results’ correlation coefficient (R) value of 0.89 and average absolute percentage error (AAPE) values of 10.23% and 10.28% in training and testing processes. While the RF technique is outperformed, as illustrated by the highest R values of 0.99 and 0.94 and the lowest AAPE values of 1.89% and 5.19% for training and testing processes, the robustness and reliability of the developed models were confirmed in the validation process with R values of 0.94 and 0.86 and AAPE values of 11.23% and 5.12% for FN- and RF-based models, respectively. The constructed models developed a basis for inexpensive static Poisson’s ratio prediction in real time with significant accuracy.

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Drill-Bit Catalog and Bit Index: A New Method for Bit Performance Evaluation

Macini

Magagni

Valente

2005

SPE Latin American and Caribbean Petroleum Engineering Conference

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe paper analyzes the application of a new method for the evaluation of bit performances. At present, the most utilized methods for bit performance evaluation are mainly based on the cost-per-foot. This approach is definitely a good reference system, but it represents only a mere economic calculation, and is not well suited for keeping into account drilling difficulties due to formation variability and directional drilling conditions. The objective of the research is both the analysis of a new drilling model for bit performance evaluation, known as Bit Index (BI), and the investigation of the possibility of BI to establish sound guidelines for the development of a mathematical tool able to help during well planning, well drilling and post-well analysis. The model was tested by mining data from the Drill-Bit Catalog (DBC) prepared at the Exploration & Production Division of Eni Group (Italy). The DBC is a database that contains both parameters for the geotechnical/geomechanical characterization of the formations and bit record/performance data. Within the DBC, three wells have been identified, recently drilled in Southern Italy. These wells have been used for the comparison and evaluation of bit performances through the BI methodology. Main results allowed to identify the bits (labeled with IADC code) that drilled with the best performances in the above three wells. The proposed model, although in its early stage of development, proved to be economic and reliable. The quality of bit performance analysis obtained with this model seems also more effective than the traditional "quick look" analysis, performed on the bit records or on the pure evaluation of the cost-per-foot.

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Formation Drillability-Definition, Quantification and Contributions to Bit Performance Evaluation

Cited by 5 publications

References 0 publications

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

Drill-Bit Catalog and Bit Index: A New Method for Bit Performance Evaluation

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