... generated. These results proved the precision of the modified equation for calculating pre-flush acid volume for reservoir acid stimulation operation. drillstem testing result production enhancement downhole intervention upstream oil & gas geometry accuracy regression parameter radial...

...) for the Nigerian, and Global correlations are 0.0033, and 0.0256 respectively. The two correlations are reliably better in terms of accuracy than the existing correlations. The new correlations would facilitate a more accurate reservoir characterization, and reliable design of surface equipment. pvt...

... equations and their parameters (distribution coefficient, C0, and the drift velocity, uD) in a modified form. The drift flux parameters were incorporated to cater for the impacts of mixing of the liquid and gas droplets. The newly developed Model predicted the critical velocity with a fitting accuracy...

...%) and testing (20%) dataset. To maximize the accuracy of the model a comparatively large enough training dataset is used in training the model. If this isn't ensured, the model could sometimes be overfitted and score very highly on the training dataset but not generalize well and score poorly of the testing...

... exhibited different forecasting accuracy for every time frame but ultimately the bagged trees and linear SVM were the superior models out performing all other models. Zhang and Hamori combined machine learning techniques and dynamic moving windows to predict crises in the US natural gas market...

... neural network optimizer accuracy elsevier Introduction Hydrocarbon production forecasting includes the estimation of the ultimate recoverable reserves and calculation of oil production profiles, which are critical factors for business planning, asset evaluation, and decision making in the oil...

... forest and support vector machine) and multi layer perceptron neural network to achieve the highest accuracy. We demonstrated the accuracy of the model by comparing the prediction results to other data driven methods. The proposed model had a better performance. Also the model has a good computational...

.... big data machine learning old generation equipment artificial intelligence dataset upstream oil & gas data mining generator accuracy digitaliztion industry 4 diesel engine fault feature dataset tank digitalization prediction algorithm consumption field operation optimization...

... us government machine learning upstream oil & gas subsea system offshore facility decommissioning artificial intelligence platform information decommissioning project dataset accuracy water depth estimation removal cost cost savings cost estimate operator partial removal...

... accurate for predicting ROP due to its ability in establishing a functional feature vector based on learning from past events. machine learning drilling operation optimization problem engineering accuracy rop neural network non-linear relationship prediction autonomous rotary drilling system...

... factor and the correspondent time. The reserves values are compared with field values for accuracy. It was observed that the result using data from long time production history accuracy was 99.98% while evaluation models built with data from short production history accuracy was 98.64%. The models...

... in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry...

... Abstract Reserves are typically estimated and re-validated throughout the life of a producing field. The accuracy of this estimation is based on the availability of relevant and current data from that reservoir or field and other factors. There are several methods for estimating reserves...

... learning models outperform the empirical models and perform accurately on unseen data from the other wells. DNN was the best performing model achieving an average accuracy of 0.987 for the 3 wells. machine learning deep learning neural network artificial intelligence cardinality drilling...

... of accuracy required by regulatory bodies. Usually, companies are either constrained to either utilize the associated gas from the oil and gas facilities as fuel gas to power up the unit or reinject in the reservoir to serve as pressure maintenance agent that pushes the oil towards the reservoir, or stored...

... for mass transport and chemical reactions. Evaluation of the developed model with other experimental and numerical results gave an excellent assessment of 95.45% for the average accuracy and 0.9933 for the average coefficient of determination. Therefore, an empirical technique to approximate the pore...

... enhanced recovery production logging accuracy sample receiver crude oil measurement uncertainty operation requirement process condition sampling representivity production control reservoir surveillance api mpm 8 mixer diameter application batch Introduction Over time, oil...

... consumption was developed. This numeric approach will increase accuracy and reduce the high simulation time often encountered in using other simulation software. To achieve this novel idea, a multilayer perceptron approach which is a deep learning neural network model built on python using Tensorflow...

... (saturated or undersaturated). Of the 34 parameters considered, it was found that the reservoir fluid composition has significant impact in determining the accuracy of the Pb-hypothesis. The key performance indicators such as MAE, RMSE, and RSE are within 0.02-0.05 and Co-efficient of determination of about...

... operation bayesian algorithm wellbore failure drilling data acquisition reservoir surveillance wellbore design wellbore stability accuracy stability confusion matrix well control artificial intelligence input data gradient application telemetry annular pressure drilling overburden stress...