Lithofluid
Web2 jun. 2015 · In Part 1 of this tutorial in the April 2015 issue of TLE, we loaded some logs and used a data framework called Pandas to manage them. We made a lithology-fluid-class (LFC) log and used it to color a … WebAfter training different MLs on the designed lithofluid facies logs, we chose a bagged-tree algorithm to predict these logs for the target wells due to its superior performance. This algorithm predicted HC units in an accurate interval (above the HC-fluid contact depth), and it showed a very low false discovery rate.
Lithofluid
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WebABSTRACT We have developed a technique to design and optimize reservoir lithofluid facies based on probabilistic rock-physics templates. Subjectivity is promoted to design possible facies scenarios with different pore-fluid conditions, and quantitative simulations and evaluations are conducted in facies model selection. This method aims to provide …
WebThe LithoFluid Probability process uses Bayesian prediction to calculate probabilities and perform classification using statistical rock physics models. Two volumes are required … Web6 sep. 2024 · to also provide a quantitative interpretation of porosity, lithology, and lithofluid facies. To improve the accuracy of reservoir property assessments and minimize uncertainties, seismic exploration deserves considerable attention. This Special Issue consists of nine studies, which could be divided into three thematic categories.
WebAbstract Mapping facies variations is a fundamental element in the study of reservoir characteristics. From identifying a pay zone to estimating the reservoir capacity, a hydrocarbon field’s development plan depends to a great extent on a reliable model of lithofacies and fluid content variations throughout the reservoir. The starting point usually … Webporosities, the sands will still be suitable for lithofluid discrimination due to the good thickness of the sands, although the sensitivity is reduced (Fig. 3-5). Figure 3 Modeling results (Negative 10 p.u scenario. Even at reduced porosity, the sands will be relatively suitable for lithofluid discrimination due to the good thickness of the sands.
WebDownload scientific diagram (a) Crossplot of PR versus I P (well-log data) showing the PDFs of each lithofluid facies. Note the poor separation between pay and nonpay …
What I do first is calculate a lithofluid-class log (LFC) in which I separate groups of data identified by similar lithologic and/or pore-fluid content. The values of the LFC log will be assigned following these rules: First I need to create the “flag” logs brine_sand, oil_sand, gas_sand and shale (these are logs … Meer weergeven To handle well-log data, I use a Python library called Pandas, which makes it very easy to manage and inspect large, complex data … Meer weergeven In this tutorial, we have laid the foundations for the real work. In * Part 2, we will look at applying Gassmann's equation to our logs to perform fluid-replacement … Meer weergeven easiest chapters for jee mains mathsWebMaximum likelihood lithofluid (with intensity) calculated using upscaled well curves. 7 - Pr Vol. Maximum likelihood lithofluid calculated using user specified absolute volumes. 8 - … ctv live ottawa newsWeb12 jun. 2024 · Keynejad et al. (2024) apply probabilistic neural networks (PNNs) and bagging trees to seismic attributes to predict lithofluid facies and confirm their higher … ctv live playerWebBased on our geologic understanding of the study area, we have augmented this initial model with lithofluid facies expected in the given depositional environment, yet not … ctv live ottawaWeb1 jun. 2015 · Scatter matrix of (a) I P and (b) V P /V S for lithofluid class 2. We can now use this information to create a brand-new synthetic data set that will replicate the average behavior of the reservoir complex and at the same time overcome typical problems when using real data such as undersampling of a certain class, presence of outliers, or … easiest cfd softwareWebCreate a lithofluid-class log. What I do first is calculate a lithofluid-class log (LFC) in which I separate groups of data identified by similar lithologic and/or pore-fluid content. The … easiest character for greed modeWebthe defined lithofluid classes to the elastic properties. Next, a fast Bayesian simultaneous AVO inversion approach is performed to estimate elastic properties and their associated uncertainties in a 2D inline section extracted from a 3D migrated seismic data set. Finally, we present and analyze the probabilistic lithology and fluid easiest character dark and darker