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Keywords: regression
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5645-MS
... design a cross-validation technique that we use to test multiple configurations of ML architectures using linear regression, support-vector regression (SVR), and random forest (RF). The results show promising potential for ML methods to assist reservoir engineers and increase the confidence in the BHFP...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3108-MS
.... The training set was fed into the ANN model and the results were compared with the results obtained from other inherently spatial methods such as universal kriging, geographically weighted regression (GWR), and generalized additive model (GAM). Finally, the EUR of the new wells were compared to the original...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3269-MS
... approach allows direct modelling of inflow performance in terms of fracture geometry, drainage volume shape, and matrix permeability. Running such a model with variable geometrical input to match data in lieu of standard regression techniques allows extraction of a meaningful parameter set for reservoir...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-148-MS
... Artificial Intelligence machine learning Upstream Oil & Gas Gupta sonic velocity different regression technique hardness Gaussian process data mining prediction extreme gradient dataset Regression Technique regression predictor application Sondergeld Oklahoma brittle zone...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-343-MS
... model was multivariate adaptive spline regression because it is fast enough to keep up with real time drilling, provides sufficiently accurate predictions, and offers clear interpretability. Average MAPE was 13% and was consistent across wells with widely varying ROP's. This bolstered our confidence...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-897-MS
... characteristics. Our results indicate that K-Means clustering yields best performance on data classification than all other tested methods while the elastic moduli estimation from Artificial Neural Network (ANN)is most accurate than Support Vector Machine (SVM), Multivariate Linear Regression (MLR...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-907-MS
... the basin and interpolated to provide a 3D volume of estimated TOC. The calculated curves and 3D model were QC'd visually and semi-statistically and found to be a reasonable match to the core data, given the methodology well logging log analysis regression URTeC core measurement machine learning...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-929-MS
... Artificial Intelligence cumulative production information neural network ensemble model predictor Upstream Oil & Gas ensemble strategy training data machine learning regression prediction unconventional reservoir society of petroleum engineers model prediction variability...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-1001-MS
... geological modeling hydraulic fracturing treatment response Completion Engineer calibrate petrophysical facies model software geologic modeling Artificial Intelligence petrophysical software facies knowledge percentile Reservoir Characterization regression information Engineer...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2877021-MS
... of clay. The data was randomly partitioned into a 70:30 split for training and validation data set respectively. Model competition among a suite of machine learning algorithms such as Linear Regression, Artificial Neural Networks (ANNs), Decision Trees, Gradient Boosting and Random Forest was used...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2896522-MS
... Intelligence strength regression ROP prediction motahhari Upstream Oil & Gas hybrid model accuracy ROP model deterministic model feature importance equation Bingham random forest inference algorithm penetration prediction data-driven model Drilling URTeC: 2896522 Rate of penetration...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2659996-MS
... to the training data set, which is used to train a set of support vector regression (SVR) models. As the sensitivity matrix for each realization can be estimated analytically from the SVR models, the DGN can use the sensitivity matrix to generate better search point such that the objective function value can...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2666809-MS
... & Simulation complex reservoir drillstem/well testing Fluid Dynamics flow in porous media oil shale regression initial slope Bakken production data MSTB interference forecasting production prediction watercut Bakken well shale oil Upstream Oil & Gas oil production Decline analysis...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2670157-MS
... or "learner" using a training data set to predict the value of an outcome based on a number of inputs, is generally referred to as "supervised learning" [4]. Such problems can be further subdivided into: regression problems, where the response variable is continuous, or classification problems, where...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2682281-MS
... Summary This paper will explore a new statistical approach to EUR estimation and show how quantile regression can greatly simplify EUR estimation, reserves estimation, completion evaluation, and production efficiency. Two key benefits include the speed with which information may be analyzed...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 1–3, 2016
Paper Number: URTEC-2460969-MS
... software and high-end machines for analysis, and the outputs are almost instant. We can use spreadsheet-based statistical add on to carry out multivariate analysis. machine learning complex reservoir lateral length well performance Upstream Oil & Gas information shale gas regression...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 1–3, 2016
Paper Number: URTEC-2460161-MS
... framework and measure rock fabric variability. These approaches can potentially identify crucial reservoir properties. Ti, Zr, K, and Al, show a general transgression in the lower 260ft with superimposed higher-frequency transgressions and regressions. This portion of the unit also show consistently high...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 1–3, 2016
Paper Number: URTEC-2426222-MS
... of petroleum engineers production data principal component analysis Reservoir Surveillance Drillstem Testing regression gas condensate reservoir boundary reservoir machine learning Artificial Intelligence complex reservoir eigenvalue diagnostic plot linear flow cumulative gas URTeC: 2426222...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 25–27, 2014
Paper Number: URTEC-1934166-MS
... guidance to the required fault offset in the field development plan. Reservoir Characterization fracture characterization Strawn Upstream Oil & Gas seismic data regression study area Midland Basin wellbore microseismic event interpretation fracture density subsurface well...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 12–14, 2013
Paper Number: URTEC-1580099-MS
... a stepwise regression analysis technique. Rock properties and structural attributes are combined with an ellipsoid stimulation model around the well bore. A stepwise regression is then used to determine what attribute has the greatest impact on the shape of the microseismic density volume. We found...

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