Artificial Intelligence Models for Real-Time Bulk Density Prediction of Vertical Complex Lithology Using the Drilling Parameters

2021 ◽  
Author(s):  
Ashraf Ahmed ◽  
Salaheldin Elkatatny ◽  
Hany Gamal ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Bulk Density ◽  
Real Time ◽  
Density Prediction ◽  
Drilling Parameters ◽  
Intelligence Models ◽  
Artificial Intelligence Models

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

2020 ◽  
Vol 12 (2) ◽  
pp. 686 ◽  
Author(s):  
Ahmed Gowida ◽  
Salaheldin Elkatatny ◽  
Saad Al-Afnan ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Bulk Density ◽  
Percentage Error ◽  
Support Vector ◽  
Drilling Process ◽  
Predictive Tool ◽  
Robust Solution ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Artificial Intelligence Models

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.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document