... completion installation and operations flow assurance erosion rate measurement and control production logging production control erosion reservoir surveillance real time system pipeline leak detection production monitoring particle workflow prediction digital twin sand production...

... This paper will focus on the PECGS AI-ML framework, which uses correlations between petrophysical data, time-dependent production characteristics, and the value of production gains to identify BCO, zones within existing wells that have not yet been produced. The predictive model estimates...

... operational parameter algorithm hyperparameter feature importance interaction analysis prediction Introduction Unconventional oil and gas resources are abundant worldwide and hold significant development potential, accounting for nearly 40% of the global energy supply ( Chen et al. 2022...

... for the analysis, which provides FLOW or NO FLOW labels, signifying successful oil or gas extraction, respectively. Once validated, the model predicts perforation outcomes in the Mutiara, Pamaguan, Badak, Nilam, Lampake, and Semberah Field, Sanga-Sanga Block, contributing to the advancement of predictive...

...-specification events and hence advise the appropriate response. The results also show that the appropriate action depends on the circumstances, making predictive simulations critical. A design analysis, as presented here, can only investigate a limited number of scenarios, leading to the conclusion...

... the comparative study against classical convolutional neural network (CNN) and long short-term memory (LSTM) models. This study combined deep learning techniques and mercury injection capillary pressure to efficiently realize the rapid intelligent prediction of CO 2 -brine RP curves that facilitate the evaluation...

... the pseudo-component and CO 2 storage mechanisms. This makes the scheme optimization tedious. Therefore, we propose a deep learning-based surrogate model to efficiently implement numerical simulation of CO 2 -flooding and storage. Proposed method consists of automatic encoder and prediction part. The auto...

... and predictive Machine Learning (ML) modeling was developed and deployed in the Montney unconventional siltstone gas reservoir, British Columbia, Canada to identify production zone "sweet spots" from reservoir quality data (i.e., geological, geophysical, and geomechanical) data and completion quality data (e.g...

... Abstract Several studies have used machine learning-based techniques to improve the production behavior prediction in existing shale gas wells. However, few studies have investigated production prediction in new wells wherein no prior information is available. This is challenging because...

... there is insufficient production data and significant subsurface uncertainty. In early field life, there is often no alternative other than analogs and operators’ experience to predict RFs. It is therefore unsurprising that the prediction of RF has considerable uncertainty, with wider ranges of RF utilised...

... laboratory studies show high agreement. The model coefficients obtained by treatment of laboratory data allow predicting skin growth in production wells under fines migration. coal seam gas reservoir characterization migration upstream oil & gas coal bed methane concentration correspond...

.... This paper is the results of our experience in H 2 S mapping at reservoir-well-facilities modelling, history matching, and prediction of H 2 S. We will highlight the workflow adopted to find the root causes of souring via sampling and modelling approach since the H 2 S is measured throughout the field across...

... Abstract An accurate pore-pressure prediction plays an important role in well planning as exploration targets shift to deeper over-pressured reservoirs. Pore pressure related problems in high-pressure high-temperature (HPHT) wells include well control, lost circulation, formation breathing...

.... The observation indicates that having an overlapping mini-cube-to-sample approach (3D cube) produces redundant data which reduces the model's ability to generalize well. When predicting less noisy data, it is evident that our model, which is as simple as a 2-layer LSTM model, can predict AI property to a high...

.... However, these methods are time-consuming and/or resource-intensive. This paper proposes a novel machine learning approach to predict permeability scores. Field drilling and wireline data are acquired from 80 wells in the Surat Basin, Australia. The permeability scores are based on petrophysical...

... removal absorbent unit, heat exchanger, gas/condensate/produced water filter, and de-oiling/de-sander hydrocyclone, are selected to present on the online dashboard for the real-time monitoring and the maintenance intervention time prediction, as per actual equipment performance. At the beginning phase...

... for calibration with actual field data to generate reliable prediction. The long-term application of INM will give greater assurance of production attainability in the L-B clustered development. floating production system gas injection upstream oil & gas floating production storage & offloading...

... platforms, seamlessly linked with Insights dashboard module in providing actionable insights, and weight predictive module supported by Machine Learning (ML) model was developed. This paper discussed the Minimum Viable Product (MVP) Phase 1 development outcome, using a close-loop weight control ecosystem...

... Sand production prediction is essential from the early stages of field development planning for completion design and later for production management. Unconsolidated and weakly consolidated sandstones are prone to fail at low flowing bottom hole pressures during hydrocarbon production. To predict...

... information from training wells to other wells ( Jain, et al., 2019 ). The workflow is divided into two (2) main steps: training and prediction. Key wells which best represent the formation in the field are used to train the model. This approach automatically generates the number of cluster (class) using...