Abstract
Petrophysical evaluation is a crucial task for reservoir characterization but it is often complicated, time-consuming and associated with uncertainties. Moreover, this job is subjective and ambiguous depending on the petrophysicist's experience. Utilizing the flourishing Artificial Intelligence (AI)/Machine Learning (ML) is a way to build an automating process with minimal human intervention, improving consistency and efficiency of well log prediction and interpretation. Nowadays, the argument is whether AI-ML should base on a statistically self-calibrating or knowledge-based prediction framework! In this study, we develop a petrophysically knowledge-based AI-ML workflow that upscale sparsely-sampled core porosity and permeability into continuous curves along the entire well interval.
AI-ML focuses on making predictions from analyzing data by learning and identifying patterns. The accuracy of the self-calibrating statistical models is heavily dependent on the volume of training data. The proposed AI-ML workflow uses raw well logs (gamma-ray, neutron and density) to predict porosity and permeability over the well interval using sparsely core data. The challenge in building the AI-ML model is the number of data points used for training showed an imbalance in the relative sampling of plugs, i.e. the number of core data (used as target variable) is less than 10%. Ensemble learning and stacking ML approaches are used to obtain maximum predictive performance of self-calibrating learning strategy.
Alternatively, a new petrophysical workflow is established to debrief the domain experience in the feature selection that is used as an important weight in the regression problem. This helps ML model to learn more accurately by discovering hidden relationships between independent and target variables. This workflow is the inference engine of the AI-ML model to extract relevant domain-knowledge within the system that leads to more accurate predictions.
The proposed knowledge-driven ML strategy achieved a prediction accuracy of R2 score = 87% (Correlation Coefficient (CC) of 96%). This is a significant improvement by R2 = 57% (CC = 62%) compared to the best performing self-calibrating ML models. The predicted properties are upscaled automatically to predict uncored intervals, improving data coverage and property population in reservoir models leading to the improvement of the model robustness. The high prediction accuracy demonstrates the potential of knowledge-driven AI-ML strategy in predicting rock properties under data sparsity and limitations and saving significant cost and time.
This paper describes an AI-ML workflow that predicts high-resolution continuous porosity and permeability logs from imbalanced and sparse core plug data. The method successfully incorporates new type petrophysical facies weight as a feature augmentation engine for ML domain-knowledge framework. The workflow consisted of petrophysical treatment of raw data includes log quality control, preconditioning, processing, features augmentation and labelling, followed by feature selection to impersonate domain experience.
