1-20 of 345
Keywords: machine learning
Close
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3014-MS
... technology, generative machine learning methods, such as those used for Deep Fake, can generate images and data that are all but indistinguishable from reality. Using an adapted generative method known as the Generative Adversarial Imputation Network (GAIN), this paper evaluates these methods and...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2782-MS
.... completion installation and operations information artificial intelligence time series data data quality upstream oil & gas signal processing technique prediction data stream enable real-time reporting procedure dataset treatment plot post-stage data slurry rate machine learning hydraulic...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2806-MS
... fracturing upstream oil & gas woodford shale constituent laboratory measurement machine learning shale gas structural geology anisotropy ratio anisotropy geomechanics symposium lithotype geophysics algorithm urtec 2806 Abstract The magnitude of elastic anisotropy in shale is a...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2976-MS
... machine learning reservoir characterization complex reservoir upstream oil & gas reconcavo basin shale vector energy economics shale oil oil shale shale data prediction basin component plane shale gas structural geology new zealand das model self-organizing map hydrocarbon...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2594-MS
... urtec 2594 fracture machine learning shale gas upstream oil & gas main hydraulic fracture shale gas well scenario workflow fracture geometry permeability Abstract Production history match can be used to evaluate effective fracture geometry and to confine the uncertainty of fracture...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2855-MS
... Abstract With the abundance of big data in the oil and gas industry, it can be sufficient to treat and solve petroleum engineering problems using data analytics. Modern data analytic techniques, statistical and machine learning algorithms have received widespread applications for solving such...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3048-MS
... simulators. However, performing these simulations at the field scale is not possible due to their computational expense. Therefore, we present a machine learning technique based on deep neural networks to predict the fluid distribution within these fractures at steady state trained upon on the lattice...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2552-MS
... computational cost, tedious modelling process and the requirement of high-performance simulation software. Machine learning-assisted computing methods have attracted significant attention during the past decade. As machine learning requires fewer input parameters while reaching better accuracy, many different...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2573-MS
... machine learning (ML) algorithms so that the algorithms can learn the underlying physics from reservoir simulation input and output. The ML model is trained such that it provides fast and scalable applications with good accuracy to find optimum unconventional field development, accounting for geological...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2786-MS
... recovery from the field and increasing the economic life of industrialized shale completions. machine learning reservoir characterization reservoir geomechanics shale oil artificial intelligence proppant hydraulic fracturing shale gas complex reservoir fracturing materials completion...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2878-MS
... machine learning production forecasting shale gas artificial intelligence complex reservoir deep learning curve analysis petroleum science dataset type curve upstream oil & gas decline curve algorithm unconventional reservoir society of petroleum engineers oil shale...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2751-MS
... method machine learning artificial intelligence hydraulic fracturing upstream oil & gas eagle ford shale ann model sensitivity analysis reduced-order model society of petroleum engineers unconventional resource technology conference sobol function URTeC: 2751 Utilizing a Global...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2937-MS
... 24.6 and 6.2 seconds, respectively, which confirms the robust performance of the metrics. reservoir characterization pixel geological modeling complex reservoir geologic modeling artificial intelligence quantification machine learning upstream oil & gas material constituent...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2763-MS
... for features segmentation using machine learning techniques. Primary facies and secondary features (e.g., pyrite) were identified from the URTeC 2763 CT images. Lazar et al. (2015) focuses on describing fine-grained sedimentary rocks using three major components texture, bedding, and composition. The...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2795-MS
... not explicitly consider hydrocarbon composition or fluid properties; rather, we let our machine learning algorithm discern implicit relationships between location, which is directly correlated to fluid properties, hydrocarbon composition, and rock properties, and controllable parameters such as...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2743-MS
... well logging machine learning artificial intelligence urtec 2743 reservoir characterization complex reservoir bakken hexane mid principal component log analysis upstream oil & gas explanation prediction proppant loading information rock quality index geoshap value bakken 3...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3036-MS
... geology, geophysics, and petroleum engineering to improve TMS reservoir and completion quality. reservoir characterization complex reservoir shale oil structural geology quartz porosity line plot calcite content machine learning artificial intelligence oil shale shale gas clay content...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3077-MS
... machine learning artificial intelligence data mining proppant reservoir simulation structural geology infill well production ratio reservoir characterization hydraulic fracturing fracturing fluid facies variability fracturing materials complex reservoir well performance depletion...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2753-MS
... Abstract Data science techniques have proven useful with the high volume of data collected in unconventional reservoir development workflows. In this paper, we present an analytics and machine learning use case for operations to minimize deferred production and quantify long-term production...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2787-MS
...), leaving significant amounts of unrecovered hydrocarbon in the subsurface. machine learning bayesian inference history matching artificial intelligence steam-assisted gravity drainage enhanced recovery sagd concentration information gas injection conductivity realization upstream oil...

Product(s) added to cart

Close Modal