scholarly journals Standardization of Uncertainty Quantification of Sub-surface Geological Models through a Field’s Life Cycle

2014 ◽  
Author(s):  
Subrata Chakraborty
Keyword(s):  
Life Cycle ◽  
Uncertainty Quantification ◽  
Geological Models

scholarly journals Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

2021 ◽  
Vol 12 (1) ◽  
pp. 479-493 ◽  
Author(s):  
Hugo K.H. Olierook ◽  
Richard Scalzo ◽  
David Kohn ◽  
Rohitash Chandra ◽  
Ehsan Farahbakhsh ◽  
...  
Keyword(s):  
Data Fusion ◽  
Uncertainty Quantification ◽  
Geophysical Data ◽  
Geological Models

scholarly journals Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020 ◽  
Vol 13 (2) ◽  
pp. 651-672 ◽  
Author(s):  
Zhen Yin ◽  
Sebastien Strebelle ◽  
Jef Caers
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Borehole Data ◽  
Geological Uncertainty ◽  
Gas Reservoir ◽  
Geothermal Resources ◽  
Automated Method ◽  
Geological Models ◽  
Statistical Relationships ◽  
Gas Volume

Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

scholarly journals Automated Monte Carlo-based Quantification and Updating of Geological Uncertainty with Borehole Data (AutoBEL v1.0)

2019 ◽  
Author(s):  
Zhen Yin ◽  
Sebastien Strebelle ◽  
Jef Caers
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Scientific Method ◽  
Borehole Data ◽  
Geological Uncertainty ◽  
Gas Reservoir ◽  
Automated Method ◽  
Geological Models ◽  
Statistical Relationships ◽  
Gas Volume

Abstract. We provide an automated method for uncertainty quantification and updating of geological models using borehole data for subsurface developments (groundwater, geothermal, oil & gas, and CO2 sequestration, etc.) within a Bayesian framework. Our methodologies are developed with the Bayesian Evidential Learning protocol for uncertainty quantification. Under such framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics and fluids), globally and spatially, without time-consuming model re-buildings. To address the above, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a data-scientific method that learns from a series of bijective operations to establish “Bayes-linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allows for fast computation posterior geological models. The proposed framework is completely automated in an opensource project. We demonstrate its application by applying to a generalized synthetic dataset motivated by a gas reservoir from Australia. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction, and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

Ultrastructural analysis of “Sensory” surface nerve endings and “putative” neurotransmitter sites in Schistosoma mansoni Miracidia

1989 ◽  
Vol 47 ◽  
pp. 962-963
Author(s):  
Betty Ruth Jones ◽  
Steve Chi-Tang Pan
Keyword(s):  
Nervous System ◽  
Life Cycle ◽  
Schistosoma Mansoni ◽  
Snail Host ◽  
Complex Life Cycle ◽  
Rational Approach ◽  
Effective Control ◽  
The Social ◽  
Social And Economic Development ◽  
Basic Biology

INTRODUCTION: Schistosomiasis has been described as “one of the most devastating diseases of mankind, second only to malaria in its deleterious effects on the social and economic development of populations in many warm areas of the world.” The disease is worldwide and is probably spreading faster and becoming more intense than the overall research efforts designed to provide the basis for countering it. Moreover, there are indications that the development of water resources and the demands for increasing cultivation and food in developing countries may prevent adequate control of the disease and thus the number of infections are increasing.Our knowledge of the basic biology of the parasites causing the disease is far from adequate. Such knowledge is essential if we are to develop a rational approach to the effective control of human schistosomiasis. The miracidium is the first infective stage in the complex life cycle of schistosomes. The future of the entire life cycle depends on the capacity and ability of this organism to locate and enter a suitable snail host for further development, Little is known about the nervous system of the miracidium of Schistosoma mansoni and of other trematodes. Studies indicate that miracidia contain a well developed and complex nervous system that may aid the larvae in locating and entering a susceptible snail host (Wilson, 1970; Brooker, 1972; Chernin, 1974; Pan, 1980; Mehlhorn, 1988; and Jones, 1987-1988).

A freeze-fracture-etched study of the physarum polycephalum plasma membrane during early formation of the macroplasmodial stage

1993 ◽  
Vol 51 ◽  
pp. 346-347
Author(s):  
Randolph W. Taylor ◽  
Henrie Treadwell
Keyword(s):  
Plasma Membrane ◽  
Life Cycle ◽  
Growth Phase ◽  
Filter Paper ◽  
Physarum Polycephalum ◽  
Freeze Fracture ◽  
Petri Dish ◽  
Wire Screen ◽  
Wide Mouth ◽  
Sterile Filter

The plasma membrane of the Slime Mold, Physarum polycephalum, process unique morphological distinctions at different stages of the life cycle. Investigations of the plasma membrane of P. polycephalum, particularly, the arrangements of the intramembranous particles has provided useful information concerning possible changes occurring in higher organisms. In this report Freeze-fracture-etched techniques were used to investigate 3 hours post-fusion of the macroplasmodia stage of the P. polycephalum plasma membrane.Microplasmodia of Physarum polycephalum (M3C), axenically maintained, were collected in mid-expotential growth phase by centrifugation. Aliquots of microplasmodia were spread in 3 cm circles with a wide mouth pipette onto sterile filter paper which was supported on a wire screen contained in a petri dish. The cells were starved for 2 hrs at 24°C. After starvation, the cells were feed semidefined medium supplemented with hemin and incubated at 24°C. Three hours after incubation, samples were collected randomly from the petri plates, placed in plancettes and frozen with a propane-nitrogen jet freezer.

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