1-20 of 141
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 Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198972-MS
... with this is that this methodology is highly subjective and it’s sometimes not data supported. In order to solve this, several statistical models have been developed with the objective of early identifying possible stuck pipe events when drilling a well. For this work, a Machine Learning approach is used in order to evaluate...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198946-MS
.... In this study, we show the robustness of POD-DEIM method to reduce the computational cost for multi-phase, multi-component 3D reservoir model. modeling & simulation fluid dynamics artificial intelligence upstream oil & gas machine learning flow in porous media secondary variable enhanced...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198960-MS
.... Our framing of the problem incorporates this knowledge into the problem systematically. Our Bayesian approach also enables us to infer the uncertainty of the deconvolved solution directly from the posterior distribution. machine learning production control production monitoring drillstem...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199005-MS
... process is time consuming and alternatives such as data-driven proxy modeling can overcome the computation complexity drawbacks. A machine learning technique called recurrent neural network (RNN) has been proved useful for reservoir modeling with sequence data. In this paper, we develop a novel end-to-end...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199061-MS
... solution id-eor project maintenance cost recommendation production network machine learning artificial intelligence steam-assisted gravity drainage upstream oil & gas information workflow opex reduction project management sagd reduction reservoir model automation injection field...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199132-MS
... that Machine Learning (ML) has, more often than not advertised as Artificial Intelligence (AI), and how well we are able to integrate it to our current workflows. So far, it seems that the industry is struggling with the second part. On the previous figure, we see how Machine Learning publications have...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199112-MS
..., such as pressures and flow rates, are changing based on production time and the fluid composition – a complex multiphase mixture composed of oil, water, and gas. Thus, it is necessary to evaluate well behavior with periodic production tests. This paper proposes an automatic tool based on machine learning models...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199107-MS
... and economic EOR potential (from exploratory appraisal to mature field rejuvenation) under conditions of limited information and time constraints. reservoir characterization machine learning core analysis asset and portfolio management chemical flooding methods artificial intelligence flow...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199118-MS
... influence reserves estimates. We present a method that incorporates this uncertainty into the analysis transparently. Our method can also be used to pool uncertainty across various well groups. production control machine learning production monitoring energy economics reservoir surveillance...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199090-MS
... wells with a total potential production upside of around 63,000 B/D. machine learning enhanced recovery reservoir characterization reservoir surveillance production forecasting reserves evaluation geological modeling production monitoring modeling & simulation drilling operation...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198943-MS
... , Thor Viggo . 1994 . Torque and drag-two factors in extended-reach drilling . Journal of Petroleum Technology 46 ( 09 ): 800 - 803 . Abadi , Martín , Barham , Paul , Chen , Jianmin . 2016 . Tensorflow: a system for large-scale machine learning . Proc ., OSDI265-283...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199047-MS
... artificial intelligence upstream oil & gas machine learning bitumen accuracy optimal eor method selection oil sand complex reservoir early production forecasting prediction workflow evaluation genetic algorithm enhanced recovery neural network heavy oil field screening...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-198997-MS
... on the basis of a similarity criterion , which is used to determine the relationship between each pair of objects in the dataset. In this section, we present a general framework for clustering in subsurface applications as an unsupervised machine learning approach. We carry out clustering at the well-level...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199062-MS
... and compares conventional straight slotted liners with seamed slotted liners at larger scale for a field. Moreover, this study helps to better understand the effect of design parameters of seamed slotted liners on sand control, flow performance and mechanical strength. machine learning sand control...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199048-MS
.... artificial intelligence reservoir characterization upstream oil & gas enhanced recovery injector predictive model pareto front algorithm objective predictivity optimization objective machine learning waterflooding optimization redistribution argentina reservoir physics scenario...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199042-MS
...-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML) based framework used with POD. The features of ML model (Random Forest used here) are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoiding simulator access...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199028-MS
... production control steam-assisted gravity drainage artificial intelligence sagd gas injection method oil shale reservoir characterization reservoir surveillance shale gas optimization problem reservoir simulation machine learning production monitoring field development optimization and planning...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199082-MS
... The objective of this paper is to develop predictive models to optimize the characterization of the SRV, discretization of the fracture network, and hydraulic fracturing modeling, by combining machine learning algorithms and reservoir engineering in low permeability reservoirs. The objectives...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199073-MS
... and recoveries under primary, improved oil recovery (IOR) and enhanced oil recovery (EOR) production schemes. geological modeling modeling & simulation geologic modeling artificial intelligence reservoir characterization porosity model tsr recovery fracture permeability machine learning...
Proceedings Papers

Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, July 27–31, 2020
Paper Number: SPE-199113-MS
... lead to a successful data mining analysis. Workovers and intervention impact on high water cut wells can be monitored more accurately. By knowing the error in the production rates, future projects will be well defined managed and evaluated. machine learning artificial intelligence reservoir...

Product(s) added to cart

Close Modal