... plastic deformation porosity composition asia government rock type wellbore integrity machine learning loading interaction dilation complex reservoir saudi arabia government mudstone compaction concentration young modeling transition reduction URTeC: 3864914 Compaction...

... is excellent, and the mean absolute percentage error for predicting the true source flux rate values of all the training images is 4.31%, for all validation images is 12.4%, and for all test images is 8.27%. Once validated against field data, a machine learning model such as this can be used as a screening...

..., influences from geology, previous fluid recovery, and inter-well spacing are less known. In this study, we use a series of machine learning models to disentangle the impacts of several well parameters and get an understanding of the conditions in which refracs produce more hydrocarbons. We first separated...

... Abstract This paper introduces a novel machine learning-driven approach, using the QLog software, to estimate pore pressure at-the-bit, in real time, as a well is being drilled. The required input data is typically available on all modern drilling rigs. Initial results suggest...

... sufficient information to aid in interpreting images for mechanical properties. With this work, we demonstrate that images contain a wealth of information that can easily be exploited for screening or quicklook analyses using machine learning. upstream oil & gas geologist deep learning...

... Abstract This analysis is the product of a state-of-the-art methodology called SHapley Additive exPlanations (SHAP), which explains the decisions the machine-learning model is making. This study develops an understanding of how liquids SHAP values are derived and how they are used to determine...

... uniformity isip fracture dimension reservoir characterization perforation machine learning far-field diverter efficiency stimulation enhancing near-field connectivity technology conference information nearfield connectivity diverter nfci optimizing completion coefficient URTeC: 3867323...

... characterization geological subdiscipline real time system fdi differentiate spe hydraulic fracturing technology conference onepetro depletion exhibition information wellbore design proppant hydraulic fracturing production logging reservoir surveillance production monitoring machine learning...

... upstream oil & gas neural network artificial intelligence architecture geological subdiscipline geology mineral machine learning classification mancos shale information detection utilizing deep learning approach conference dataset calcite accuracy university characteristic mineral...

... Abstract We have developed a data driven workflow for forecasting future cumulative oil production in the unconventional asset development planning in the Permian basin. The workflow involves data ETL (exploration, transformation, and loading), machine learning (ML), uncertainty quantification...

... months using stimulated lateral length (SLL) alone or SLL and total proppant (SLLandTP). The analysis utilizing statistical and machine learning techniques involves evaluating the impact of well spacing, sequencing, and landing zone TVD on four KPIs, with comparisons made between individual plays...

... drilling agent rl algorithm simulation engine algorithm machine learning optimization algorithm international drilling conference proximal policy optimization simulator URTeC: 3865879 Maximizing Efficiency of Deep-Reinforcement Learning Agents in Autonomous Directional Drilling...

... exhibition efficiency breakdown machine learning technology conference multiple type event miou URTeC: 3863378 Auto-Identification and Real-Time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing Mingze Zhao1, Yue Li1, Yuyang Liu2,3, Bin Yuan1, Siwei Meng2, Wei...

... models. After TFT synthesizes a stable production profile, long-term production can be predicted using decline curve analysis. Then, automated machine learning (AutoML) is used to model the relationship between geological and engineering factors and long-term production. Controlling factors that affect...

... Formation in the northern Midland Basin and associated earthquake activity. The hybrid method leverages petrotechnical and machine-learning-(ML-) based workflows to characterize earthquakes. The novel process uses information such as geocellular reservoir properties, reservoir simulation data, historical...

... development. This paper will walk through one operator’s approach to answering some of these questions through use of RTA (Rate Transient Analysis), statistical methods, and machine learning in the Duvernay and Bakken formations. In this workflow, multiphase RTA is performed and the LFP (Linear Flow...

.... The present study provides insights on how depletion status and completion parameters affect parent-child interaction and productivity, which can be used to guide the optimal design of child well completions. The analysis developed a novel machine-learning-based predictive model to estimate the production...

... to mitigate it. The problem objective is to predict when a child well frac operation will interact and affect a neighbor parent well using data-driven Machine Learning (ML) algorithms. This solution requires extensive subject matter expert (SME) validated data to create a ML training data set that can...

...-LSTM models were built, and an optimization algorithm was used to determine the hyperparameters of the optimal model in production prediction. Finally, machine learning (ML) classifier, Random Forest (RF) were used to investigate the efficacy of forecasting. The results reflect promising predictions...

...) and siliciclastic channel, levee, and fan lobe (fine grained sandstones) (Montgomery, 1997). geologist sedimentary rock delaware basin carbonate rock clastic rock united states government artificial intelligence reservoir simulation upstream oil & gas sand drilling operation machine learning...