...) and they concluded that this method can be used to predict the ROP with a very good accuracy even in the absence of some parameters. The obvious drawback of this method is the great need of huge amount of data to train the network and still no correlation is deduced only the value of ROP as an output. Fear...

... 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...

..., 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...

... 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. The constructed ANN’s predictability...

... 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...

... 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...

... 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...

... for the drilling performance per field and set certain benchmarks to gauge the speed of any newly drilled well. To date, no solid or reliable model exists because of the complexity of the drilling process, and one cannot capture every factor to predict the rate of penetration. Therefore, the utilization...

... different methods of ML to predict the PVBT using type, length, diameter, porosity, and permeability of the core; HCl concentration; temperature; and pressure. Moreover, in reality, the wormholing phenomenon in carbonates is complex and challenging due to several factors, such as rock heterogeneity...

... 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...

... ). 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 a detailed understanding of acid-rock reactions, rock strength...

... the input along with other unrelated data categories. Accordingly, the following parameters were finalized to be used as the input: Data Acquisition, Preparation and Analysis Machine Learning techniques have successfully been shown to predict oil rate in an artificial lift gas well...

... 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...

... 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...

... correlation works extremely well in prediction mode with the shale affected log data as inputs. A comprehensive numerical and illustrative evaluation of the claimed accuracy is shown along with the error analysis between both the machine learning techniques used. (1) S w n = R w...

... 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...

... 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...

... is a combination of input, hidden and output layers; each layer has a certain number of neurons. The ANN network is trained utilizing the input data and different learning algorithms, then, the output values can be predicted ( MathWorks, Inc 2008 ). ANFIS or adaptive neuro-fuzzy-inference system is a type...

... The petroleum reservoir property prediction process needs very high degree of precisions that any minor variation from anticipation may consequence in enormous wastage and losses of man-hours and colossal investments. On contrary, a slight enhancement in the prediction scenarios will have...

... the measurement units, equal to 8.345. g: the gravitational acceleration, equal to 9.8 m/s 2 . Such numerical evaluations for predicting ECD values did not take into account many other factors affecting ECD while drilling, as implementing these factors in the equations will increase the error factors...