...URTeC: 4029160 Applying Numerical RTA to Public Data: Enabling Field-Wide Property Calibration and Improved Public Data EUR Forecasts Braden Bowie*1, Jordan Bowie2, Mathias Lia Carlsen3, 1. APA, 2. ARC, 3. Whitson Copyright 2024, Unconventional Resources Technology Conference (URTeC) DOI 10.15530...

... learning (ML) models with the traditional method of type curves (TC) for forecasting new well production in the Appalachian Basin. The primary objectives are to evaluate predictive accuracy, computational efficiency, and enhancements to decision-making when utilizing ML versus type curves. We gathered...

...URTeC: 4036541 A Machine Learning Approach to Basin Scale Inventory Forecasting and Development Planning that Accounts for Complex Well Interactions Charles Connell1, Kyle LaMotta1, Clark Munger2, Joe Wicker2, 1. PetroAI, 2. Vital Energy Inc. Copyright 2024, Unconventional Resources Technology...

... produced at a higher CGR than the other wells, and subsequently was excluded from the baseline data set. Figure 20: CUM CGR of empirical case study example only Mid Montney wells shown. Finally, to compare production across multiple timesteps up to 5 years of producing time, forecasted well performance...

... deep learning geologist complex reservoir reservoir surveillance neural network production monitoring machine learning production control dataset depletion function prediction forecast workflow geology artificial intelligence depletion urtec sensitivity accuracy validation...

... Abstract Minimizing aggregated error in PDP forecasts is often the largest controllable driver of success in oil and gas acquisitions. Estimating aggregated error in PDP forecasts begins with using a repeatable non-subjective automated forecasting method and backtesting the results...

... production forecasting complex reservoir inflow performance performance indicator unconventional resource economics well performance energy economics streamlining type curve freeborn calculation strategic planning and management forecast benchmarking russell enhanced production...

... degradation, inefficiencies from long laterals, and parent-child interactions. This study utilizes advanced machine learning to analyze these factors and forecast remaining inventories. Our findings challenge the narrative of production decline, attributing it to temporarily elevated outputs in 2020-2021 due...

..., despite the importance of these wells for NPV calculations or supply projections, we do not have an accurate method of forecasting these wells with traditional decline curves. In this study, we compare a machine learning method to traditional curve fitting to determine the potential for accuracy...

... Abstract This study aims to assess the influence of pressure-dependent permeability (PDP) on production forecasts in source-rock reservoirs, specifically focusing on the impact of decreasing pore pressure on pore volume, PDP, and productivity. Given the unique characteristics of source rocks...

... methodology can be applied for an overall efficiency balance. In this study, an established ML based rate-transient analysis approach is applied to achieve rapid asset forecast for area of interest. Public Permian Basin data were used for initial well assessment. A synthetic data set was used to build...

... forecast unconventional well interference detection drillstem/well testing reservoir simulation modeling & simulation sankaran algorithm physics informed data-driven model sathish sankaran Abstract Well interference in unconventional reservoirs has been a pivotal issue due to its...

... have changed, necessitating a significant increase of electric load demand within the Permian Basin. The workflow consists of (1) creating a power demand forecast by applying power requirements (HP or kW) for various upstream and downstream oil and gas operations to a production forecast, (2...

... completion to bigger 3,200 gal/ft – 2,400 lb/ft completion size. The predicted production forecasts from the simulation models were sent to the asset team to run economics, predicting well and section economic metrics to optimize development at various commodity prices in the DJ basin. Introduction...

... inflow performance geologist gas lift geology complex reservoir drillstem testing forecast upstream oil & gas united states government drillstem/well testing pvt measurement artificial intelligence asset and portfolio management production monitoring workflow production...

... states government health & medicine unconventional resource technology conference upstream oil & gas drillstem testing depletion drillstem/well testing interaction basin dataset complex reservoir parent-child depletion explanation forecast eur artificial intelligence seg...

... forecast united states government complex reservoir production monitoring energy economics mape urtec upstream oil & gas artificial intelligence machine learning information presented reservoir surveillance canada government technology conference accuracy unconventional...

... clastic rock upstream oil & gas sedimentary rock geology geologist complex reservoir health & medicine machine learning optimizing shale infill development county ensemble frac fingerprint hydraulic fracturing reservoir characterization prediction assumption forecast...

... machine learning models that have demonstrated accuracy to actual well production. In this effort, Shell technical experts created complete datasets, configured ML models, and generated ML-derived forecasts for multiple development scenarios. Several combinations of subsurface features and completions...

... Abstract Managing unconventional assets necessitates accurate production forecasting. Traditionally, Decline Curve Analysis (DCA) and statistical type curves are used to estimate productivity for existing and productivity new wells, respectively. However, those approaches are inadequate...