... decision-makers, facilitating informed investment decisions. The research employs a Bayesian model to predict gas production variables under different development scenarios and with both informative and non-informative priors. Various time data slices are selected from different points in the Basins...

..., sonic log data is not commonly available for the horizontal Marcellus shale wells due to the complexity and cost. The objective of this study is to predict the anisotropic minimum horizontal stress by utilizing drilling parameters including depth, weight-on-bit (WOB), revolution per minute (RPM...

... Machine Learning (ML) techniques. This study was conducted in three phases, employing a range of machine learning techniques to develop the optimal model for predicting surface treating pressure during hydraulic fracturing and use that to optimize the FR concentration. In phase 1, utilizing data from...

..., encounter significant difficulties in accurately predicting fracture distributions, and high-fidelity simulations can be computationally prohibitive. This paper proposes a comprehensive machine learning-based workflow to effectively characterize and describe fracture distributions for unconventional...

... integration of production, completion, subsurface and spatial data using machine-learning algorithms to predict production performance for future development wells. The internal Marcellus Business Unit (MBU) well database, populated with data of 500+ historical wells, has been used in this study. Production...

... of the stress shadowing, stage sequencing, and the mechanical properties on gas production from multi-stage hydraulic fractured horizontal well completed in Marcellus Shale. A commercial hydraulic fracturing software was employed to predict the fracture properties for a multi-stage hydraulic fractured...

... by casing motion, pipe rotation, eccentricity and cuttings. Therefore, the objective of this research is to develop data-driven statistical learning models which can be very effective in making pressure loss predictions for given operating conditions. The data for proposed supervised learning was obtained...

... through co-kriging of decline curve parameter values, where the parameter values are obtained at each existing well by fitting an appropriate decline model to the production history. Co-kriging gives the best linear unbiased prediction of parameter values at undrilled locations, and also estimates...

... practices, challenges, and novel applications under development to overcome industry deficiencies. This study encompasses a comprehensive compilation of data mining algorithms and industry applications from a predictive analytics standpoint using supervised and unsupervised advanced analytics algorithms...

... processing pipeline granularity software machine learning Upstream Oil & Gas optimization application development plan big data model accuracy pipeline algorithm Artificial Intelligence completion prediction completion design Optimization Opportunity proppant society of petroleum...

... Abstract Reliable and fast prediction of fractured well performance is vital for asset evaluation and prudent field development decision making in unconventional, low permeability oil and gas reservoirs. Simple low cost techniques such as decline curve analysis are ill suited of properly...

... Abstract A method for predicting torque and drag has been developed which is based on statistical learning models and which utilizes parameters routinely measured while drilling. Real time prediction of torque and drag during drilling has particular significance in on-the-fly rig floor...

... Abstract Hydraulic fracturing has increased immensely in recent years. An accurate prediction of frictional pressure losses of fracturing slurries is crucial for successful treatment and to avoid premature screen-out or even treatment failure. Scarce data and lack of theoretical basis of slurry...

... b is a function of the vertical to horizontal permeability ratio. The new model is tested against random simulations that are not included in the original simulation matrix for the model development, and better agreement is observed compared to the predictions of previous IPR's available...

... Table 1 The calculated parameters in Bowers’ model for predicting pore pressure max stress 6500 A 4.456 B 0.899 U 3.13 Table 2 The calculated parameters in Over-Consolidated Model (OCM) for predicting pore pressure Field A’ const B'const 1 2.7 0.92...

... Abstract Reservoir simulators are effectively used in predicting the production performances of oil and gas reservoirs within a good level of accuracy. However, during initial stages of exploitation, our knowledge on most of the reservoir properties and production parameters involve a good...

... 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...

... reservoir production control complex reservoir Reservoir Surveillance gas storage composition storage operation Reservoir Structure storage cycle prediction evaluation Appalachian Basin gas condensate reservoir reservoir pressure model prediction operation compositional analysis reservoir...

... to improved decisions and resource management. The workflow involves identifying key parameters influencing uncertainties in recovery predictions through an Experimental Design/Response Surface methodology. The need to do a global sensitivity analysis motivates the workflow presented in our study...

... Ratio olaberinjo reservoir volumetric balance reservoir Upstream Oil & Gas gas condensate reservoir coefficient productivity complex reservoir Compressibility Factor prediction condensate compressibility calculation thermal expansion coefficient fraction pvt measurement production...