Approach to Perforation Interval Selection Uses AI-Assisted Image Interpretation

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216856, “ML-Driven Integrated Approach for Perforation Interval Selection Based on Advanced Borehole-Image AI-Assisted Interpretation,” by Alexander Petrov, SPE, Mounir Belouahchia, SPE, and Abdelwahab Noufal, SPE, ADNOC. The paper has not been peer reviewed. _ In the complete paper, the authors propose an artificial-intelligence (AI)-assisted work flow that uses machine-learning (ML) techniques to identify sweet spots in carbonate reservoirs. This process involves annotation of geologic features using a well database, with supervision from subject-matter experts (SMEs). The resulting ML model is tested on new wells and can identify pay zones, perforation intervals, and stress analysis. The models successfully detect fractures, breakouts, bedding planes, vugs, and slippage passages with pixel-level precision, reducing borehole-image (BHI) analysis time. BHI Interpretation and Preprocessing The use of BHIs requires manual interpretation and data identification, heavily relying on the expertise and time of SMEs. A widely adopted approach to address this challenge is the use of supervised computer-vision algorithms, a subfield of AI. These algorithms optimize the task function or model based on examples they have learned from data during training. However, when applied to BHIs, certain ML challenges must be addressed, including the following: - Detecting features in wells from different reservoirs using a model trained on wells from one reservoir can be highly challenging because reservoirs may exhibit distinct geological characteristics. - The handling of parts of BHIs with missing data, depicted by vertically slanted white strips, poses considerable difficulty. Therefore, the authors created a deep-learning approach based on a generative adversarial network architecture to fill the gaps automatically (Fig. 1). - The labels provided by geologists often do not have pixelwise precision, causing the machine to become confused while trying to learn inconsistent patterns. The authors use a convolutional neural network (CNN) to compute a probability map for pixels belonging to specific classes. In this application, a class is defined as any of the heterogeneities in the BHI; however, this method is applicable to any type of heterogeneities in an image. After training, the CNN module provides the optimal probability for each pixel in the image. To classify regions in the BHI based on heterogeneities, a class-specific threshold is established. Pixels with values above the thresholds are assigned to the corresponding class, while those below the thresholds are assigned to the background. BHI-Derived Porosity A new approach for borehole-derived porosity was developed in-house to overcome the limitations of existing techniques widely used in the industry. This approach capitalizes on BHIs for multiple analyses, including structural dip assessment, fault and fracture identification, and determination of minimum and maximum horizontal stress orientation. However, its primary strength lies in quantifying the fraction of secondary porosity in heterogeneous or dual-porosity carbonate formations. The authors have devised a novel method that uses borehole electrical images to conduct porosity and image connectivity analysis. By implementing this technique, essential information can be extracted regarding the spatial distribution of porosity and the extent of secondary porosity fraction.