Hasil untuk "Geology"

C. Zou, D. Dong, Shejiao Wang et al.

With Sichuan Basin as focus, this paper introduces the depositional environment, geochemical and reservoir characteristics, gas concentration and prospective resource potential of three different types of shale in China: marine shale, marine-terrigenous shale and terrigenous shale. Marine shale features high organic abundance (TOC: 1.0%–5.5%), high-over maturity (Ro: 2%–5%), rich accumulation of shale gas (gas concentration: 1.17–6.02 m3/t) and mainly continental shelf deposition, mainly distributed in the Paleozoic in the Yangtze area, Southern China, the Paleozoic in Northern China Platform and the Cambrian-Ordovician in Tarim Basin; Marine-terrigenous coalbed carbonaceous shale has high organic abundance (TOC: 2.6%–5.4%) and medium maturity (Ro: 1.1%–2.5%); terrigenous shale in the Mesozoic and Cenozoic has high organic abundance (TOC: 0.5%–22.0%) and mid-low maturity (Ro: 0.6–1.5%). The study on shale reservoirs in the Lower Paleozoic in Sichuan Basin discoveried nanometer-sized pores for the first time, and Cambrian and Silurian marine shale developed lots of micro- and nanometer-sized pores (100–200 nm), which is quite similar to the conditions in North America. Through comprehensive evaluation, it is thought that several shale gas intervals in Sichuan Basin are the practical targets for shale gas exploration and development, and that the Weiyuan-Changning area in the Mid-South of Sichuan Basin, which is characterized by high thermal evolution degree (Ro: 2.0%–4.0%), high porosity (3.0%–4.8%), high gas concentration (2.82–3.28 m3/t), high brittle mineral content (40%–80%) and proper burial depth (1500–4500 m), is the core area for shale gas exploration and development, the daily gas production for Well Wei 201 is 1×104–2×104 m3.

E. Hoek, J. Bray

H. Machel

Yaoguo Li, D. Oldenburg

S. McKeever

W. Berggren, D. Kent, M. Aubry et al.

E. Sundquist, W. Broecker

C. M. White, Duane H. Smith, K. Jones et al.

M. Mckinney

R. Summons, L. Jahnke, J. Hope et al.

G. Badarch, W. Dickson Cunningham, B. Windley

F. Gradstein, J. Ogg, Alan G. Smith et al.

M. Cracknell, A. Reading

Machine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Hence, there is much scope for the application of MLAs to the rapidly increasing volumes of remotely sensed geophysical data for geological mapping problems. We carry out a rigorous comparison of five MLAs: Naive Bayes, k-Nearest Neighbors, Random Forests, Support Vector Machines, and Artificial Neural Networks, in the context of a supervised lithology classification task using widely available and spatially constrained remotely sensed geophysical data. We make a further comparison of MLAs based on their sensitivity to variations in the degree of spatial clustering of training data, and their response to the inclusion of explicit spatial information (spatial coordinates). Our work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data. Random Forests is straightforward to train, computationally efficient, highly stable with respect to variations in classification model parameter values, and as accurate as, or substantially more accurate than the other MLAs trialed. The results of our study indicate that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically. The use of explicit spatial information generates accurate lithology predictions but should be used in conjunction with geophysical data in order to generate geologically plausible predictions. MLAs, such as Random Forests, are valuable tools for generating reliable first-pass predictions for practical geological mapping applications that combine widely available geophysical data.

M. Carr, J. Head

R. Zhu, Yigang Xu, Guang Zhu et al.

J. Alcalde, S. Flude, Mark Wilkinson et al.

Carbon capture and storage (CCS) can help nations meet their Paris CO2 reduction commitments cost-effectively. However, lack of confidence in geologic CO2 storage security remains a barrier to CCS implementation. Here we present a numerical program that calculates CO2 storage security and leakage to the atmosphere over 10,000 years. This combines quantitative estimates of geological subsurface CO2 retention, and of surface CO2 leakage. We calculate that realistically well-regulated storage in regions with moderate well densities has a 50% probability that leakage remains below 0.0008% per year, with over 98% of the injected CO2 retained in the subsurface over 10,000 years. An unrealistic scenario, where CO2 storage is inadequately regulated, estimates that more than 78% will be retained over 10,000 years. Our modelling results suggest that geological storage of CO2 can be a secure climate change mitigation option, but we note that long-term behaviour of CO2 in the subsurface remains a key uncertainty. Carbon capture and storage can help reduce CO2 emissions but the confidence in geologic CO2 storage security is uncertain. Here the authors present a numerical programme to estimate leakage from wells and find that under appropriate regulation 98% of injected CO2 will be retained over 10,000 years.

A. Raza, R. Gholami, R. Rezaee et al.

Abstract Excessive emission of greenhouse gases into the atmosphere has resulted in a progressive climate change and global warming in the past decades. There have been many approaches developed to reduce the emission of Carbon Dioxide (CO2) into the atmosphere, among which Carbon Capture and Storage (CCS) techniques has been recognized as the most promising method. This paper provides a deeper insight about the CCS technology where CO2 is captured and stored in deep geological formations for stabilization of the earth's temperature. Principles of capturing and storage for a long-term sequestration are also discussed together with the processes, mechanisms and interactions induced by supercritical CO2 upon injection into subsurface geological sites.

Ju Wang, Liang Chen, R. Su et al.

Abstract With the rapid development of nuclear power in China, the disposal of high-level radioactive waste (HLW) has become an important issue for nuclear safety and environmental protection. Deep geological disposal is internationally accepted as a feasible and safe way to dispose of HLW, and underground research laboratories (URLs) play an important and multi-faceted role in the development of HLW repositories. This paper introduces the overall planning and the latest progress for China's URL. On the basis of the proposed strategy to build an area-specific URL in combination with a comprehensive evaluation of the site selection results obtained during the last 33 years, the Xinchang site in the Beishan area, located in Gansu Province of northwestern China, has been selected as the final site for China's first URL built in granite. In the process of characterizing the Xinchang URL site, a series of investigations, including borehole drilling, geological mapping, geophysical surveying, hydraulic testing and in situ stress measurements, has been conducted. The investigation results indicate that the geological, hydrogeological, engineering geological and geochemical conditions of the Xinchang site are very suitable for URL construction. Meanwhile, to validate and develop construction technologies for the Beishan URL, the Beishan exploration tunnel (BET), which is a 50-m-deep facility in the Jiujing sub-area, has been constructed and several in situ tests, such as drill-and-blast tests, characterization of the excavation damaged zone (EDZ), and long-term deformation monitoring of surrounding rocks, have been performed in the BET. The methodologies and technologies established in the BET will serve for URL construction. According to the achievements of the characterization of the URL site, a preliminary design of the URL with a maximum depth of 560 m is proposed and necessary in situ tests in the URL are planned.

I. Halevy, A. Bachan

J. Warren