... Abstract This paper presents an experimental study to test and validate a technique of predicting rock grain size distribution (GSD) from nuclear magnetic resonance (NMR) measurements and digital rock modeling. GSD is one of the basic parameters in geo-science and petroleum engineering...

... two Artificial Neural Network (ANN) based proxy models. Namely, a proxy for performance prediction that utilizes specific injection design parameters to predict reservoir performance and an inverse proxy for injection design that provides injection design scheme for a desired reservoir performance...

..., and to reduce the nonproductive time (NPT). ROP depends on many variables such as; drilling parameters, fluid properties, and formation strength. Previous models of ROP did not include the effect of the change in rock mechanics on the ROP prediction. Several models were developed based on a regression...

... Machine Learning techniques have successfully been shown to predict oil rate in an artificial lift gas well with a coefficient of determination of approximately 96-99% when the results were tested against actual separator measured flowrate values. Among all the computational intelligence...

... of a wellbore, a constitutive model is required to estimate the stresses around the wellbore coupled with a failure criterion to predict the ultimate strength of reservoir rocks. The Mohr-Coulomb failure criterion is one of the commonly accepted criteria for rock strength estimation at a given state of stress...

... Khoukhi in 2012 compared extreme learning machines (ELM) and radial basis function networks (RBF) on Bourgoyne and Young's model, and showed that RBF gave best results in terms of accuracy and processing time. Monazami in 2014 used ANN feed-forward network on one well to predict ROP using 13...

... have been spent on estimating acid fracture conductivity. Abstract Acidizing joint hydraulic fracturing is a widely used technique to increase the fracture conductivity in carbonate reservoirs stimulation. Predicting acid fracturing performance for optimum fracturing job design requires...

... Abstract Rate of penetration (ROP) is the main function that affects drilling operation economically and efficiently. Many theoretical models reported in the literature were produced to predict ROP based on different parameters. Most of these models used only drilling parameters to estimate ROP...

... downhole conditions. The objective of this paper is to present a new technique for predicting ECD values while drilling without the need for downhole tools. The technique uses surface drilling parameters in a model to evaluate the ECD values precisely using artificial intelligence (AI) techniques...

... empirical models reported in the literature were used to calculate the fracture pressure based on different parameters. Most of these models used only formation and rock properties to estimate fracture pressure. Other models predicted the fracture pressure based on log data using a few real field data...

... Abstract The Inflow Performance relationship is considered one of the diagnostic tools used by Petroleum engineers to evaluate the performance of a flowing well. An accurate prediction of well IPR is very important to determine the optimum production scheme, design production equipment...

... to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure...

... in %. Conclusion Machine Learning techniques have successfully been shown to predict water saturation with a correlation of determination of approximately 94% in the case of ANN and 96% using ANFIS. A new empirical model for estimating water saturation has been proposed which can be applied to any case...

..., such as the mud weight, the Marsh funnel viscosity, and solid content, are measured regularly and continuously. Therefore, it is valuable to come up with a relation that relates the mud rheological properties to these parameters. Many researchers tried to introduce models that allow for the prediction...

... model FNBEM, forward backward model FNFBM and backward forward model FNBFM. Then based on correlation coefficient R and average absolute percentage error AAPE, a comparison was made and the one which performed better was selected for prediction of sonic wave transit times. The novelty of this work...

... significantly outperforms all other existing models and delivers the predictions with high accuracy. A small average absolute percentage error of less than 2% was found with new proposed empirical correlation, while comparing the existing published correlations on the same data gives more than 15% error...

... correlations. In this study, artificial intelligence techniques were used to estimate the cutting concentration in the wellbore. The purpose of this study is to use Support Vector Machine (SVM) technique to indirectly measure the hole cleaning efficiency by predicting the cutting concentration in the wellbore...

... or empirical) were developed to relate the drilling parameters with the rate of penetrations and to predict the drilling efficiency. However, these models are arrived at by ignoring some parameters or employing simplifying assumption(s), which may lead to over or under optimistic drilling performance...

... a reliable model to predict the MMP during CO 2 flooding. Actual case studies for flooding heterogeneous and anisotropic reservoir were utilized to generate the MMP model, more than 140 data points were used to construct and evaluate the proposed model. Several artificial intelligence techniques were...

... with the minimum volume. In a linear coreflood experiment, this translates into a dominant wormhole dissolution pattern. Outside the range of such rate, the pattern could be face dissolution, conical wormhole, ramified wormhole, or uniform dissolution. Researchers have been trying to develop models to predict...