A New, Data-Driven Methodology for the Integration of Seismic and Well Data Into Geological Model - Case Study Block 4, Offshore Angola, Lower Congo Basin

2020 ◽  
Author(s):  
Mario de Sá ◽  
Lidia. J Ferreira ◽  
Manuel. N José ◽  
Joào Simào ◽  
Wasiu Adedayo Sonibare ◽  
...  
Keyword(s):  
Congo Basin ◽  
Data Driven ◽  
Geological Model ◽  
Model Case ◽  
Well Data

Drilling success as a result of probabilistic lithology and fluid prediction—a case study in the Carnarvon Basin, WA

2008 ◽  
Vol 48 (1) ◽  
pp. 31 ◽  
Author(s):  
Matthew Lamont ◽  
Troy Thompson ◽  
Carlo Bevilacqua
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Geological Model ◽  
Probabilistic Prediction ◽  
Individual Probability ◽  
Fluid Properties ◽  
Lithology Prediction ◽  
Well Data ◽  
Carnarvon Basin

The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.

scholarly journals Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74471-74481 ◽  
Author(s):  
Zaher Mundher Yaseen ◽  
Wan Hanna Melini Wan Mohtar ◽  
Ameen Mohammed Salih Ameen ◽  
Isa Ebtehaj ◽  
Siti Fatin Mohd Razali ◽  
...  
Keyword(s):  
Data Driven ◽  
Tropical Region ◽  
Model Case ◽  
Hybrid Data ◽  
Streamflow Simulation

scholarly journals Digital Storytelling in Cultural Heritage: Audience Engagement in the Interactive Documentary New Life

2021 ◽  
Vol 13 (3) ◽  
pp. 1193
Author(s):  
Anna Podara ◽  
Dimitrios Giomelakis ◽  
Constantinos Nicolaou ◽  
Maria Matsiola ◽  
Rigas Kotsakis
Keyword(s):  
Cultural Heritage ◽  
Full Range ◽  
Social Dimension ◽  
Data Driven ◽  
Specific Interest ◽  
Key Factors ◽  
Audience Engagement ◽  
Main Research ◽  
The Social

This paper casts light on cultural heritage storytelling in the context of interactive documentary, a hybrid media genre that employs a full range of multimedia tools to document reality, provide sustainability of the production and successful engagement of the audience. The main research hypotheses are enclosed in the statements: (a) the interactive documentary is considered a valuable tool for the sustainability of cultural heritage and (b) digital approaches to documentary storytelling can provide a sustainable form of viewing during the years. Using the Greek interactive documentary (i-doc) NEW LIFE (2013) as a case study, the users’ engagement is evaluated by analyzing items from a seven-year database of web metrics. Specifically, we explore the adopted ways of the interactive documentary users to engage with the storytelling, the depth to which they were involved along with the most popular sections/traffic sources and finally, the differences between the first launch period and latest years were investigated. We concluded that interactivity affordances of this genre enhance the social dimension of cultural, while the key factors for sustainability are mainly (a) constant promotion with transmedia approach; (b) data-driven evaluation and reform; and (c) a good story that gathers relevant niches, with specific interest to the story.

scholarly journals Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study

2021 ◽  
Vol 296 ◽  
pp. 126242
Author(s):  
Oliver J. Fisher ◽  
Nicholas J. Watson ◽  
Laura Porcu ◽  
Darren Bacon ◽  
Martin Rigley ◽  
...  
Keyword(s):  
Data Driven ◽  
Multiple Target ◽  
Process Manufacturing ◽  
Target Data

scholarly journals Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Nilgün Güdük ◽  
Miguel de la Varga ◽  
Janne Kaukolinna ◽  
Florian Wellmann
Keyword(s):  
Structural Parameters ◽  
Rock Properties ◽  
Magnetic Data ◽  
Geological Model ◽  
Borehole Data ◽  
Likelihood Functions ◽  
Different Types ◽  
Geophysical Information ◽  
Different Sources

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.

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