..., Neural Network models are then developed to study the impacts of all parameters on gas production as well as perform history matching of the field history. The AI assisted model with acceptable matching of field data can be used to model different hydraulic fracturing design scenarios and provide...

... and stimulation parameters in a dynamic manner only according to the field data. A database of Marcellus shale reservoir is generated by integrating information such as well locations, well trajectories, reservoir characteristics, completion, hydraulic fracturing, and production parameters, etc. Neural network...

... modifications are also presented. Both standard and machine learning techniques were used to analyze the results. neural network vertical permeability calculation machine learning reservoir simulation cutoff simulator flow in porous media fluid dynamics simulation artificial intelligence...

... and estimate reservoir properties. We demonstrated the versatility and applicability of our proposed approach with synthetic and field cases. pressure transient analysis neural network pressure transient testing machine learning Upstream Oil & Gas deconvolved response diagnostic plot...

... Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Gaussian Processes (GP)) were applied to understand the non-linear patterns in the data. The objective was to develop predictive models that were trained and validated based on the current database. The predictive models were validated using...

... operations and handling of extremely large data bases, hence, facilitating tough decision-making processes. machine learning google patent Artificial Intelligence information node operation prediction decision tree automation neural network dataset Exhibition algorithm probability...

..., and (c) shape of rock inclusions (i.e., grains and pores). Core measurements are used for cross validating the well-log-based estimates of elastic moduli and petrophysical properties. Accordingly, we proposed a rock classification technique using unsupervised neural network that integrated depth-by-depth...

... of parameters such as reservoir characteristics, operational activities on the ultimate recovery determination in shale gas reservoir. production monitoring Reservoir Surveillance machine learning production control complex reservoir cumulative production neural network information production data...

... developed a smart proxy based on artificial neural networks (ANNs) for fast analysis of estimated ultimate recovery (EUR) and NPV. Although the outcome of our study is subjective to the chosen asset, the workflow provides a good example of horizontal well spacing and hydraulic fracturing design optimization...

... Recognition hydraulic fracturing neural network shale gas Well Quality analysis ultimate recovery machine learning complex reservoir completion Reservoir Characteristic dataset society of petroleum engineers lateral length reservoir design parameter shale well decline curve analysis...

... design parameters in a given shale asset. As the first step CDC-EUR is estimated. In the second step data-driven analytics using artificial neural networks is employed to condition the CDC-EUR to rock properties, well characteristics, and completion design parameters. Then, artificial Intelligence...

... employs neural network (ANN) modeling techniques to develop a predictive model to identify performance drivers and evaluate completion effectiveness. Sensitivities performed on the predictive ANN model developed for this project, indicate that well to well variation in reservoir quality and geology has...

... complex reservoir individual well history lateral production rate completion history matching marcellus shale entire field spe 161184 calibration shale reservoir model neural network reservoir model hydraulic fracturing modeling data mining hydrocarbon production optimum history society...

... to improve and enhance gas recovery. big sandy gas field upstream oil & gas history machine learning society of petroleum engineers artificial intelligence lower huron shale neural network pattern recognition reservoir modeling modeling & simulation database eastern kentucky...

... Intelligence machine learning coal bed methane neural network reservoir simulator co 2 simulation run SRM coalbed methane reservoir simulation information coal seam gas Upstream Oil & Gas input parameter artificial neural network reservoir permeability simulator Injection Rate cumulative...

..., and then, performs particle swarm optimization (PSO) to refine the results. The data conversion scheme is implemented by a neural network ensemble using the EC-PSO-derived LD outputs as training targets. This method has better capability to tackle problems of local minima and to produce robust conversion of the new...

.... Artificial Intelligence coalbed methane machine learning flow in porous media coal bed methane Upstream Oil & Gas neural network water saturation flow rate simulation run artificial neural network coal seam gas Fluid Dynamics prediction application expert system complex reservoir sand...

... in a compositional, dual-porosity reservoir simulation model. A set of representative design scenarios is created and run using this model. Then, the collected performance indicators are fed into the neural network for training and two neural network-based proxies are developed: 1) A forward proxy to predict...

... includes an easy to use interface that allows the user to edit the data for a gas storage field, perform well-test analysis and use neural networks in association with Genetic optimization tool. The software ranks the well according to maximum change in skin value and recommends the best stimulation slurry...

.... A synthetic seismic model is developed by using real data and seismic interpretation. In the example presented here, the model represents the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the Buffalo Valley Field in New Mexico. Generalized regression neural network (GRNN) is used...