Environmental Reviews and Case Studies: Addressing the Societal Costs of Unconventional Oil and Gas Exploration and Production: A Framework for Evaluating Short-Term, Future, and Cumulative Risks and Uncertainties of Hydrofracking

2012 ◽  
Vol 14 (4) ◽  
pp. 352-365 ◽  
Author(s):  
Simona L. Perry
Keyword(s):  
Case Studies ◽  
Oil And Gas ◽  
Short Term ◽  
Societal Costs ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Exploration And Production ◽  
Unconventional Oil And Gas ◽  
Cumulative Risks

scholarly journals Static Geological Modelling with Knowledge Driven Methodology

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3802
Author(s):  
Jun Li ◽  
Xiaoying Zhang ◽  
Bin Lu ◽  
Raheel Ahmed ◽  
Qian Zhang
Keyword(s):  
Case Studies ◽  
Oil And Gas ◽  
Fracture Network ◽  
Depositional Environments ◽  
Soft Data ◽  
Oil And Gas Exploration ◽  
Hard Data ◽  
The North ◽  
Geological Modelling ◽  
Exploration And Production

Geological modelling is an important topic of oil and gas exploration and production. A new knowledge driven methodology of geological modelling is proposed to address the problem of “hard data” limitation and modelling efficiency of the conventional data driven methodology. Accordingly, a new geological modelling software (DMatlas) (V1.0, Dimue, Wuhan, China) has been developed adopting a grid-free, object-based methodology. Conceptual facies models can be created for various depositional environments (such as fluvial, delta and carbonates). The models can be built largely based on geologists’ understandings with “soft data” such as outcrops analysis and geological maps from public literatures. Basic structures (fault, folds, and discrete fracture network) can be easily constructed according to their main features. In this methodology, models can be shared and re-used by other modelers or projects. Large number of model templates help to improve the modelling work efficiency. To demonstrate the tool, two case studies of geological modelling with knowledge driven methodology are introduced: (1) Suizhong 36-1 field which is a delta depositional environment in Bohai basin, China; (2) a site of the north Oman fracture system. The case studies show the efficiency and reliability within the new methodology.

Classification of Tight Sandstone Reservoir Based on the Conventional Logging

2014 ◽  
Vol 633-634 ◽  
pp. 526-529 ◽  
Author(s):  
Xiao Ling Xiao ◽  
Jia Li Cui ◽  
Yu Peng Zhang ◽  
Xiang Zhang ◽  
Han Wu
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Identification Accuracy ◽  
Support Vector ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Unconventional Oil And Gas ◽  
Sandstone Reservoir

With the increasing social demand for oil and gas resources, the exploration and development of unconventional oil and gas reservoirs will pay more and more attention. Tight sandstone reservoir classification is one of the important tasks in the research of unconventional oil and gas exploration and development.Limitations exist in tight sandstone reservoir classification by various conventional logging.A method for the classification of tight sandstone reservoir based on support vector machine is presented in this paper, combining with the core data and flow unit to establish the reservoir classification standard. Tight sandstone reservoirs of no coring wells are classified based on the model made by support vector machine using conventional logging.The application results show that this method has high suitability and identification accuracy.

Risk In Exploration And Production - Eliminating Risk Is Not An Option - It's How You Manage It That Matters!

2015 ◽  
Author(s):  
Lars SØrum
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Development ◽  
Oil And Gas Exploration ◽  
Risk Levels ◽  
The Us ◽  
Comparative Advantages ◽  
Exploration And Production ◽  
Unconventional Oil And Gas ◽  
Below Ground ◽  
And Performance

Abstract The paper aims to provide insights as to what risk elements are observed in the US shale and tight oil and gas development and how they sit in a European setting. In doing so the paper explores the comparative advantages in prescriptive and performance based approaches for shale risk management and through discussing consequence based vs. risk based philosophies. The author uses metrics for below ground risks to compare risk levels. In doing so the paper aim to qualify what what is perceived as risk and what risks are measured in unconventional oil and gas exploration and production.

Machine learning technique for the quantitative evaluation of tight sandstone reservoirs using high-pressure mercury-injection data

2020 ◽  
Author(s):  
JingJing Liu ◽  
JianChao Liu
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Quantitative Evaluation ◽  
Oil And Gas ◽  
Tight Sandstone ◽  
Reservoir Evaluation ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Unconventional Oil And Gas ◽  
Sandstone Reservoirs

<p>In recent years, China's unconventional oil and gas exploration and development has developed rapidly and has entered a strategic breakthrough period. At the same time, tight sandstone reservoirs have become a highlight of unconventional oil and gas development in the Ordos Basin in China due to its industrial and strategic value. As a digital representation of storage capacity, reservoir evaluation is a vital component of tight-oil exploration and development. Previous work on reservoir evaluation indicated that achieving satisfactory results is difficult because of reservoir heterogeneity and considerable risk of subjective or technical errors. In the data-driven era, this paper proposes a machine learning quantitative evaluation method for tight sandstone reservoirs based on K-means and random forests using high-pressure mercury-injection data. This method can not only provide new ideas for reservoir evaluation, but also be used for prediction and evaluation of other aspects in the field of oil and gas exploration and production, and then provide a more comprehensive parameter basis for “intelligent oil fields”. The results show that the reservoirs could be divided into three types, and the quantitative reservoir-evaluation criteria were established. This method has strong applicability, evident reservoir characteristics, and observable discrimination. The implications of these findings regarding ultra-low permeability and complex pore structures are practical.</p>

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