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Keywords: regression
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4042562-MS
... is approximately 3000 m (TVD). clastic rock sedimentary rock mathematics of computing structural geology geologist drilling operation reservoir simulation reservoir characterization rock type vaca muerta formation unconventional resource technology conference eur ultimate recovery regression...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4043567-MS
... fractional dimension rate transient analysis transition regression figure 10 nonlinear regression compressibility seg unconventional resource technology conference URTeC: 4043567 Fractional Dimension Rate Transient Analysis (FD-RTA) Applied to Multiphase Wells Jorge A Acuña*1, Vincent Artus2...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862919-MS
... reservoir characterization hydrocarbon productivity facies thickness bone spring formation structural geology geointegra consulting rock type montgomery fairfield geotechnology geological subdiscipline control regression non-linear regression saturation new mexico URTeC: 3862919 Predicting...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3870194-MS
... is the potential fracture width inversion from field data. Estimation of fracture width at the monitor well location from fiber-optic strain response in the stress shadow zone is a regression problem. Thus, we use four different supervised machine learning algorithms in this work: multilinear regression, support...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3725863-MS
... of porosity and permeability. Different machine learning algorithms have been developed including Linear Regression (LR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Adaptive Booster Regressor (AdaBoost), and Support Vector Regression (SVR), to predict...
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
... drilling reserves evaluation urtec 3269 drillstem/well testing unconventional resource economics university of tulsa regression contribution artificial intelligence geometry exponential decline decline curve analysis interpretation shale gas noise reservoir simulation upstream oil & gas...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-897-MS
... while the elastic moduli estimation from Artificial Neural Network (ANN)is most accurate than Support Vector Machine (SVM), Multivariate Linear Regression (MLR) and Multivariate Adaptive Regression Spine (MARS). The dimension reduction became essential when then input datasets are remarkably correlated...
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
... variety of techniques such as artificial neural networks, high performance random forest, linear logistic regression, support vector regression, multiple adaptive regression spline, gradient boosting machine, deep learning and recurrent neural networks have been used – although only Ref. [5] and [9] used...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-1001-MS
.... I. Uniaxial Data Relationships One method for using data relationships analysis in statistical software packages to quickly identify meaningful uniaxial relationships as detailed in Edwards et al. (2019). To summarize, a combination of linear regression and Spearman rank correlations are used...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-148-MS
... overhead. The compressional and shear velocities (Vp and Vs respectively) can be used to calculate Young s modulus, Poisson s ratio and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have compared...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-343-MS
...-out-cross-validation by training on 45 of the wells and blind testing on the remaining five. Mean Absolute Percentage Error (MAPE) was used to compare the models because it allows a percentage estimate of ROP prediction accuracy. The winning model was multivariate adaptive spline regression because...
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...

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