Integrating statistical rock physics and pressure and thermal history modeling to map reservoir lithofacies in the deepwater Gulf of Mexico

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. IM15-IM28 ◽  
Author(s):  
Wisam H. AlKawai ◽  
Tapan Mukerji ◽  
Allegra Hosford Scheirer ◽  
Stephan A. Graham
Keyword(s):  
Gulf Of Mexico ◽  
Thermal History ◽  
Rock Physics ◽  
Training Data ◽  
Basin Modeling ◽  
Spatial Trends ◽  
Single Well ◽  
Lithofacies Classification ◽  
Statistical Rock Physics

We have developed an approach integrating statistical rock physics with pressure and thermal history modeling for quantitative seismic interpretation (QSI). Extending the training data for lithofacies classification and deriving distributions for scenarios not available in the original training require knowledge about geologic processes affecting the elastic properties in the subsurface. We model pressure and thermal history and corresponding smectite to illite diagenesis with a basin model across Thunder Horse minibasin in the Gulf of Mexico. By comparing the mapped lithofacies with and without basin-modeling extrapolations against the results of a reference workflow, we found the value of integrating basin modeling results and statistical rock physics with QSI workflows. The reference workflow uses all available data from two wells in the QSI. The first workflow performs the same lithofacies classification with data from only a single well and does not account for spatial trends away from the well. In the second workflow, we use data from only a single well, the same well as in the first workflow, and bring in extrapolation from the basin and petroleum system modeling at the location of the second well. Results for the first workflow indicate significant differences with the reference workflow in the training data, the quality of the inverted impedance volumes, and the classified reservoir lithofacies. In the second workflow, the guided extrapolation of the training data accounts for spatial trends away from the well and the quality of the impedance inversion significantly improves. The predicted lithofacies map in this scenario shows only minor differences from the reference workflow, and the posterior probabilities of lithofacies show less uncertainty compared with the first workflow. The superiority of the second workflow demonstrates the added value of the integration workflow to QSI in cases of spatially limited well control.

Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 988-1001 ◽  
Author(s):  
T. Mukerji ◽  
A. Jørstad ◽  
P. Avseth ◽  
G. Mavko ◽  
J. R. Granli
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
Rock Physics ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Training Data ◽  
Well Log ◽  
Statistical Rock Physics

Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

Effects of hypoxia on habitat quality of pelagic planktivorous fishes in the northern Gulf of Mexico

2014 ◽  
Vol 505 ◽  
pp. 209-226 ◽  
Author(s):  
H Zhang ◽  
DM Mason ◽  
CA Stow ◽  
AT Adamack ◽  
SB Brandt ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Habitat Quality ◽  
Northern Gulf Of Mexico

scholarly journals CATH functional families predict functional sites in proteins

2020 ◽  
Author(s):  
Sayoni Das ◽  
Harry M Scholes ◽  
Neeladri Sen ◽  
Christine Orengo
Keyword(s):  
Functional Characterization ◽  
Functional Site ◽  
Training Data ◽  
Supplementary Information ◽  
Conserved Residues ◽  
Functional Sites ◽  
Protein Protein Interaction ◽  
Evolutionary Features ◽  
Functional Families

Abstract Motivation Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein–protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). Results FunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. Availabilityand implementation https://github.com/UCL/cath-funsite-predictor. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 ◽  
Vol 69 (4) ◽  
pp. 297-306
Author(s):  
Julius Krause ◽  
Maurice Günder ◽  
Daniel Schulz ◽  
Robin Gruna
Keyword(s):  
Active Learning ◽  
Near Infrared ◽  
Agricultural Products ◽  
Training Data ◽  
Calibration Model ◽  
Learning Approaches ◽  
Training Samples ◽  
Agricultural Applications ◽  
Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

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