39
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Coal macrolithotypes are closely correlated with coal macerals and pore–fracture structures, which greatly influence the changes in gas content and the coal structure. Traditional macrolithotype identification in coalbed methane (CBM) wells mostly depends on core drilling observation, which is expensive, time-consuming, and difficult for broken core extraction. Geophysical logging is a quick and effective method to address this issue. We obtained coal cores from 75 wells in the deep regions of the Jiaozuo Coalfield, northern China, quantitatively analyzed the logging cutoff number corresponding to various macrolithotypes, and established natural γ (GR), deep lateral resistivity (LLD), and γ–γ log (GGL) response rules for each coal macrolithotype. The formation mechanisms of different coal macrolithotypes are discussed from the perspective of coal facies and pore structures. The results show that GGL decreased but GR and LLD increased from bright coal to dull coal. Most coal macrolithotypes can be distinguished based on the established thresholds of various logging curves. However, excessively high or low ash yields significantly affect the validity of identification. The vertical coal macrolithotypes attributed to the peat marsh environment in Shanxi Formation mostly comprise three to six sublayers; dull or semi-dull coals are predominant close to the 2 1 coal seam, and the bright or semi-bright types usually appear in the middle part. The semi-bright and bright coals are usually vitrinite rich, whereas the semi-dull and dull coals are primarily inertinite rich. For pore structure arguments, the highest average specific surface area ( S BET) and the total pore volume ( V BJH) are found in bright coals, followed by dull and semi-bright coals; those of semi-dull coals are the lowest. However, S BET and V BJH change significantly for different samples, even though the coal macrolithotype is the same. Therefore, the macrolithotype is not the key factor determining the coal parameters of pore structures. Rapid and effective identification of coal macrolithotypes can help determine the CBM enrichment area, the CBM well location, and the exploration horizon.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: not found
          • Article: not found

          Coalbed methane sorption related to coal composition

            • Record: found
            • Abstract: not found
            • Article: not found

            Variation in micropore capacity and size distribution with composition in bituminous coal of the Western Canadian Sedimentary Basin

              • Record: found
              • Abstract: not found
              • Article: not found

              On the fundamental difference between coal rank and coal type

                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                13 December 2021
                28 December 2021
                : 6
                : 51
                : 35523-35537
                Affiliations
                []Liaoning Technical University , Fuxin 123000, China
                []China University of Mining and Technology , Beijing 100083, China
                [§ ]No. 3 Exploration Team, Henan Administration of Coal Geology , Zhengzhou 450016, China
                Author notes
                Author information
                https://orcid.org/0000-0002-9891-979X
                Article
                10.1021/acsomega.1c05012
                8717541
                34984284
                8b24e709-a705-4812-b1d5-32b4a16c5c78
                © 2021 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 13 September 2021
                : 25 November 2021
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 42102223
                Funded by: Discipline Innovation Team of Liaoning Technical University, doi NA;
                Award ID: LNTU20TD-30
                Funded by: Discipline Innovation Team of Liaoning Technical University, doi NA;
                Award ID: LNTU20TD-14
                Funded by: Discipline Innovation Team of Liaoning Technical University, doi NA;
                Award ID: LNTU20TD-05
                Funded by: China Postdoctoral Science Foundation, doi 10.13039/501100002858;
                Award ID: 2021M693844
                Categories
                Article
                Custom metadata
                ao1c05012
                ao1c05012

                Comments

                Comment on this article

                Related Documents Log