... are behind the drill bit. This can impair the drilling efficiency and subsequently increase non-productive time (NPT). In this paper, a machine learning approach is implemented to generate the gamma ray log (regression task) and identify coals (classification task) during drilling. The data is first filtered...

... estimates. We present the application to production data of tight-oil wells building an average predictive distribution for the estimated ultimate recovery (EUR). production forecasting artificial intelligence machine learning complex reservoir model uncertainty upstream oil & gas two-phase...

... complexity fracture wellbore propagation bedding fracture net pressure index machine learning complex reservoir hydraulic friction coefficient operation pressure curve pressure curve URTEC-208320-MS Study on Diagnosis Model of Shale Gas Fracture Network Fracturing Operation Pressure Curve Ran...

... depletion state well spacing machine learning upstream oil & gas value driver complex reservoir future development well solution space sector model resource density artificial intelligence decision framework model well spacing study ranking methodology section subsurface realisation...

... on a given pressure, temperature, and overall mole fraction) is computationally expensive. So, various researchers have considered using machine learning models trained with the rigorous phase-equilibrium (flash) calculations to improve computational speed. Unlike previous publications that apply classical...

..., and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach – a combination of XGBoost and Factor Contribution Analysis (FCA) - that not only...

... hydraulic fracturing treatment data, completion and reservoir engineers and geoscience teams obtain meaningful results accelerating the learning curve and are provided a large parametric population for multivariant analysis and machine learning applications. The methodology presented provides a low-cost...

.... In this study, one of the most classic unsupervised machine learning methods namely, principal component analysis (PCA), was combined with radial basis function neural network (RBFN), which is a low computational complexity deep-learning method (RBFN-PCA) in forecasting the AOFP for shale gas wells. This method...

..., and versatile machine learning (ML) methods in a predictive study. Apparent viscosity for NP-CO2 foam is determined while considering all input parameters. In this work, we compare the performance of four data-driven non-linear ML algorithms: Multilayer Perceptron Neural Network, Support Vector Regression, K...

...URTEC-208343-MS A Comparative Study of Machine Learning Model Results and Key Geologic Parameters for Unconventional Resource Plays Jeff Bowman, Optima Resources Inc.; Hamed Tabatabaie, IHS Markit; Julie Anna Bowman, Optima Resources Inc. Copyright 2021, Unconventional Resources Technology...

... indicates that the proposed method provides a good reference for the rational production allocation and stable production of gas wells. artificial lift system reservoir surveillance upstream oil & gas machine learning artificial intelligence engineering production control production logging...

... East. flow in porous media reservoir characterization upstream oil & gas fluid dynamics machine learning oilfield microscopic pore structure low permeability carbonate reservoir interlayer application artificial intelligence thick layered reservoir carbonate reservoir...

...: this means using data both to mitigate risks, to increase operational efficiency, by improving processes, and to create new business models. We propose a new approach where expert geologists collaborate with data scientists to develop a tool based on Machine Learning (ML) and Artificial Intelligence (AI...

.... The experimental adsorption measurements were conducted at three different temperatures up to 60°C and pressure up to 200bar. The variation of adsorption on shale fabric has been conducted using different machine learning approaches. The multivariate analysis is carried out using the partial least square (PLS...

... Abstract The rise of modern machine learning has inspired many applications in various fields including petroleum. Many researchers have recently tried utilizing machine learning in general and deep learning in specific for petroleum production time series forecast. One challenge of this task...

... 25.5×10 4 m 3 /d, which is 15 times larger than that of the adjacent well JS317. Field application shows that the proposed technology providing a robust mode to exploit unconventional reservoir. completion installation and operations engineering machine learning complex reservoir hole...

... forecasting, economic rates, and recoveries of petroleum reservoirs. machine learning artificial intelligence reserves replacement us government reservoir characterization asset and portfolio management modeling & simulation complex reservoir shale reservoir water saturation petroleum...

... geomorphology, channel boundaries, and faults might not be fully visible, resulting in misleading labeled data and incorrect interpretations. Hence, in this work, we propose the application of 3D machine learning models to solve the problem of seismic facies classification. This introduces a two-fold challenge...

... to production performance prediction and fracturing optimization. hydraulic fracturing fracture length reservoir characterization upstream oil & gas machine learning complex reservoir fracture cluster artificial intelligence orientation fracture network sweet spot fracture identification...

... failure date. These findings enable gas operators to arrange the maintenance and replacement of the pumps, reducing the downtime and thereby increasing the production of gas wells. artificial lift system complex reservoir machine learning upstream oil & gas progressing cavity pump early...