... vertical dotted line full co 2 viscosity pure co 2 specification concentration correlation coefficient prediction fluid composition solid line pipeline and well thermodynamic and transport property Introduction CCS projects developed as gathering networks include different sources of CO...

... deep learning machine learning artificial intelligence architecture transformer prediction dataset efficiency multi-scale learning segmentation mask semantic segmentation task smoke detection smoke mask segmentation conference flare smoke detection smoke region relu-based linear...

... R z ¯ T ¯ To change the unit to U.S field unit, the bottom hole pressure is predicted in psia and the q sc is in MSCFD, then it becomes P w f 2 = E x p ( s ) P h f 2 + 6.67 × 10 − 4 f M [ E x p ( s ) − 1 ] q s c...

... combinations of various common salts (chlorides, carbonates/bicarbonates, and sulphates) as they are seen in real formation brines and water utilities. The prediction from ML models were validated against the available experimental data that were not used in training. The main results are as follows...

... as density measurements using a Coriolis meter. The experimental results were compared with numerical simulation predictions using an in-house program and commercial software, DrillSIM TM . The results show a good agreement between the experimental data, including the DTS measurements, and the numerical...

... spe-iadc managed pressure drilling complex reservoir exhibition simulator malaysia prediction drilling and underbalanced operation conference surface back pressure conference and exhibition annual technical conference and exhibition underbalanced operation conference technology conference...

..., many existing reduced-order modeling methods, such as embed to control (E2C) and embed to control and observe (E2CO), fall short in long-term predictions due to the accumulation of prediction errors over time. This issue arises partly from the one-step prediction framework inherent in E2C and E2CO...

...: The prediction of CO 2 solubility in brine from the phase equilibria model (developed here) matches very well with experimental data, within the accuracy of 5.34% for single salt and 4.14% for mixed salts. The water condensation rate (WCR) across the wellbore height changes with the brine composition...

... the conventional effective stress formation pressure prediction methods, where the principal stress is the vertical load OB can lead to serious mishaps. The lack of cohesive seismic velocity due to the salt perturbation added uncertainty to any predrilling velocity – pressure prediction transformation modeling...

... shows a field application of machine learning (ML) algorithms using triple combo logs to predict elastic properties and finally estimate in-situ stress profile in the Natural Buttes gas field. We used a complete set of triple combo and dipole sonic logs to train K-Nearest Neighbors (KNN) regression...

..., it is time-consuming, inconvenient, and complicated. This work aims to enhance the prediction efficiency and accuracy of MMP between CO 2 and crude oil under reservoir conditions by improved and integrated machine-learning approaches. A novel method is proposed to improve the forecasting accuracy...

... design. The first two steps are enabled by experimentally generating CO 2 exposure data on multiple cement systems and exposure conditions. Data for alteration rates, physical changes, mechanical changes, etc. are gathered in order to build semi-empirical models that can predict the effect of CO 2...

... management quantification geological subdiscipline accuracy probability machine learning architecture risk and uncertainty assessment reservoir characterization prediction uncertainty quantification petroleum engineer attention mechanism neural network application seismic stratigraphy...

... surrogate model ℕℕ f for fast and accurate prediction of the multiphase flow in porous media, and an inference network ℕℕ g that intakes prior subsurface knowledge and performs history matching based on the pretrained ℕℕ f . We examine the performance of the DNN-HM workflow with a two-phase water-flooding...

... Abstract This study aims to utilize machine learning algorithms to predict drilling parameters, specifically Weight on Bit (WOB), Flowrate, and Revolutions Per Minute (RPM), for an undrilled well within a geological formation. This prediction aims to maximize the Rate of Penetration (ROP...

.... In the fault detection module, a hybrid approach was adopted by combining measured and simulated data generated using physics-based models, where a machine learning model is trained to detect anomalies and generate alarms. In the prognostics module, a degradation state variable is introduced to predict...

... on an Azure-hosted data manager for almost a year, after extensive testing and achieving adequate level of performance. geology geologist artificial intelligence deep learning heavy oil play unconventional play petroleum play type hydraulic fracturing prediction classification integrity...

..., such as the cuttings bed and wellbore trajectory, etc., resulting in low accuracy. In addition, these methods are primarily real-time calculation of friction coefficient rather than prediction in advance for analyzing the stuck pipe trend, leading to a lack of timeliness. To solve these problems, a prediction method...

... Abstract Real-time permeability prediction and flow risk assessment is useful both for the safety of drilling operations as well as operational challenges like determination of pressure point depths and perforation zones. A model giving accurate permeability predictions based solely on LWD...

... categorization artificial intelligence accuracy deep learning large language model supply chain optimization provider category petroleum engineer adaptability prediction expenditure drilling llm society natural language invoice cost management completion dataset Introduction The oil...