... adoption of well log responses for identifying the reservoir lithology. Most Machine learning approaches are imminently built to render good classification, and some have been adapted to probability estimation. The purpose of this study is to demonstrate how machine learning can be used to estimate...

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... drilling conference galveston ensemble operation probability texas Introduction As interest around reducing greenhouse gas emissions continues to increase, so has the need for reliable and accurate models used to make decisions to reduce them. As laid out in a number of publications ( Allen...

..., machine learning context. This results in a powerful tool, illustrated here in evaluating the failure risk of a component of a downhole tool. drilling operation upstream oil & gas artificial intelligence machine learning probability risk and uncertainty assessment personnel competence...

... breakthrough thickness sequestration invasion simulated probability Introduction Subsurface geological sequestration has been recognized as one of the most approachable and effective methods to realize long-term CO 2 capture ( IPCC, 2018 ). However, its field application is limited...

.... upstream oil & gas performance indicator contract drilling operation strategic planning and management operator drilling equipment benchmarking tier contractor rig drilling performance contract deviation alignment personnel probability petroleum engineer partnership evolution...

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... a "Risk Burndown" approach based around an initial assessment of Probability of Success (POS), different Paths to Deployment (PTD) are developed. A Value of Information (VOI) approach is used to evaluate key metrics. This leads to a diverse set of de-risking options with associated trade-offs for informed...

... presented in this paper together with a simplified application to a subsea well. risk management upstream oil & gas probability operational safety uncontrolled leak wellbore design frequency wellbore integrity workover instantaneous frequency information intensity evaluation dhsv...

... established physics-based models of the system and the details from past observed/monitored failures to evaluate future risk potential for the area. In our approach, we establish the current probability for the state of stress for the area under investigation, then monitor how the state of stress evolves...

... scenario history matching artificial intelligence optimization schlumberger 2020 field development plan evaluation reservoir simulation machine learning automated reservoir model calibration assessment subsurface uncertainty applied schulze-riegert workflow calculation probability objective...

.... Workflow Data acquisition programs (field appraisal) have two main objectives: (1) to increase the Probability of Economic Success (PES) above the respective company's Probability of Economic Success (PES) hurdle ( Steineder at al. 2019 ) (2) improve the value of the field (re-)development (e.g...

... models (scenarios, objectively evaluates their probabilities, and helps to plan optimal exploration/appraisal activities. knowledge management reservoir characterization estimates of resource in place resource in place estimate information risk management upstream oil & gas nereite...

... of misinterpretation by BiLSTM is successfully corrected in the post-processing by imposing the transition frequencies. The mean absolute error after post-processing is only 3.9 meters. Once all necessary data is interpreted and successfully mapped into the 3D model, the next step is to create the probability...

... to the issues that matter. Understanding the issues in the context of materiality and then what to do, if anything, about an issue, becomes key to maximizing success. This approach cuts waste and focuses the attention on what matters. Decision Intelligence not only increases the probability of making the best...

.... presented show a more accurate weight for each probability, suggesting that the same methodology can also be implemented for the lognormal distribution. We use the following notation through this work, where x and y are the discrete random variables lognormally distributed that were built using...

... permeability reservoirs. upstream oil & gas clay content porosity distribution permeable core sample enhanced recovery mineral content porosity reservoir bs 10 2 probability reservoir rock sedimentation flow in porous media average value reservoir property core sample reservoir...

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... on Gradient Boosting decision trees. The model analyzes real time drilling parameters within a sliding 4-hour window. For each measurement, the model calculates the probability of an accident and warns about anomaly of particular type, if the probability exceeds the selected threshold. Our training sample...

... exacerbating the issue is that we do not know what we have for production or reserves in our long reach unconventional horizontal wells until after we have tied-in and produced them. This effectively eliminates the Pg and Pmcfs (probability of finding the minimum commercial volume or more) branches in Figure...