GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Guo, Jianhong  (5)
  • 1
    Online Resource
    Online Resource
    Evaluation of Coalbed Methane Content by Using Kernel Extreme Learning Machine and Geophysical Logging Data
    Hindawi Limited ; 2022
    In:  Geofluids Vol. 2022 ( 2022-10-8), p. 1-28
    Details
    In: Geofluids, Hindawi Limited, Vol. 2022 ( 2022-10-8), p. 1-28
    Abstract: Accurate evaluation of coalbed methane (CBM) content plays a momentous role in the identification and efficient development of favorable exploitation blocks of CBM resources, but there are still many technical challenges in the exploration and development of onshore CBM fields. With the development and application of geophysical logging technology, using geophysical logging data to predict the gas content of CBM reservoirs has been proven to be an effective and feasible solution. However, the complex logging response of the CBM reservoirs makes it difficult to characterize the relationship between the gas content and the logging curve response by a simple linear relationship. In this paper, kernel extreme learning machine (KELM), a machine learning method, is combined with the geophysical logging data to predict the vertical variation curve of gas content in CBM wells. In this paper, the laboratory data on coal rock gas content from 12 CBM wells in the Southern Shizhuang block are selected, and a CBM content prediction model based on the KELM method is constructed by selecting the log curves, combining cross-validation and grid-seeking to determine the hyperparameters, and validating the prediction model using the test dataset and a new well in the same block. The application of the model on the test dataset was remarkable, and the vertical variation of CBM content obtained by applying it to the new well was consistent with the laboratory results, which proved the correctness and generalizability of the model. The results of this paper show that the CBM content evaluation model based on the KELM method and geophysical logging data is applicable to the 3# coal seam in the target block and can be used to predict the vertical CBM content of CBM wells; compared with the extreme learning machine (ELM) method and the backpropagation neural network (BPNN) method, the KELM method requires fewer hyperparameters to be explored when constructing the CBM content evaluation model, and the model construction is simple and has high prediction accuracy. At the same time, the CBM content model constructed by the KELM method differs for different blocks, coal seams at different depths, and different response ranges of geophysical logging data. The construction of a CBM content prediction model using the KELM method and logging curves is an effective means of characterizing CBM resources, and the model construction process and evaluation criteria studied in this paper can be used to help other blocks evaluate the CBM content, providing guidance for further exploration and development of CBM fields with practical application.
    Type of Medium: Online Resource
    ISSN: 1468-8123 , 1468-8115
    URL: Article
    DOI: 10.1155/2022/3424367
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045012-6
    SSG: 13
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Saturation Determination and Fluid Identification in Carbonate Rocks Based on Well Logging Data: A Middle Eastern Case Study
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 4 ( 2023-04-20), p. 1282-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 4 ( 2023-04-20), p. 1282-
    Abstract: In the Middle East, there remain many technical challenges in the water saturation evaluation of carbonate rocks and the effective identification of reservoir fluid properties. The traditional Archie equation is not applicable to carbonate reservoirs with complex pore structures and varying reservoir space distribution, as there are obvious “non-Archie” phenomena. In this paper, by analyzing the experimental data on the rock resistivity of the target formation in the study area and analyzing the relationship between stratigraphic factors and porosity, the previous fitting method was modified as a result of using the actual data while avoiding the cementation index as a way to improve Archie’s formula to evaluate the water saturation. Based on the improved Archie formula, the mathematical differential operation of water saturation and porosity was carried out using the formation resistivity. The calculation results of irreducible water saturation were used to calibrate the oil layer, and the water layer was calibrated when the water saturation was 100%, allowing for a novel reservoir fluid property identification method. This total differential method can effectively identify the oil-down-to (ODT) and water-up-to (WUT) levels in an oil–water system and then accurately divide the transition zone of the oil–water layer. When this method was applied, the identification results were in good agreement with production conclusions and test data with an accuracy rate of 89.95%. Although the use of geophysical logging data from open-hole wells combined with the total differential method is only applicable to wells with similar logging time and production time, it is possible to compare geophysical logging data from different periods to construct oil–water profiles to observe the changes in ODT over time to guide development and adjust production plans. The proposed reservoir fluid property identification method and the improved water saturation calculation formula can meet the requirements of water saturation evaluation in the target block with low calculation cost and easy implementation, which provides a new method for water saturation evaluation and rapid identification of reservoir fluid properties.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11041282
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    DataSheet6.xlsx
    Frontiers Media SA ; 2023
    In:  Frontiers in Earth Science Vol. 10 ( 2023-1-6)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 10 ( 2023-1-6)
    Abstract: The content of industrial components of coalbeds, one of the main parameters of coalbed methane (CBM) reservoirs, is crucial in the entire coal mine resource exploration and exploitation process. Currently, using geophysical logging data to determine the content of industrial components is the most widely implemented method. In this study, the PZ block in the Qinshui Basin was employed as a target block to evaluate ash (A ad ), fixed carbon (FC ad ), volatile matter (V daf ), and moisture (M ad ) under the air-dry (AD) base condition based on the autocorrelation between the geophysical logging curves and industrial component contents combined with the OBGM (1, N) model. The results indicate that 1) the geophysical logging curves combined with the OBGM (1, N) model can accurately predict the A ad and FC ad contents and an increase in geophysical logging curve types can effectively improve the model performance, compared to using a single geophysical logging curve for prediction. 2) When predicting the V daf content, using the geophysical logging curves combined with A ad and FC ad contents had the highest prediction accuracy. Further, prediction bias does not exist, compared to using only the geophysical logging curve or the autocorrelation between the industrial component contents. The entire evaluation process begins with an assessment of the A ad and FC ad contents. Then, the V daf content was assessed using the content of these two industrial components combined with geophysical logging data. Finally, the M ad content was calculated using the volumetric model. Accurate application results were obtained for the verification of new wells, demonstrating the efficacy of the method and procedure described in this study. 3) The OBGM (1, N) model has the highest prediction accuracy compared with the multiple regression and GM (0, N) models, which have the same computational cost. The geophysical logging interpretation model of the proposed coalbed industrial component contents is simple to calculate and suitable for small samples, providing a new method for the evaluation process of industrial component contents.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/feart.2022.1031218
    DOI: 10.3389/feart.2022.1031218.s001
    DOI: 10.3389/feart.2022.1031218.s002
    DOI: 10.3389/feart.2022.1031218.s003
    DOI: 10.3389/feart.2022.1031218.s004
    DOI: 10.3389/feart.2022.1031218.s005
    DOI: 10.3389/feart.2022.1031218.s006
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741235-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Evaluation of irreducible water saturation by electrical imaging logging based on capillary pressure approximation theory
    Elsevier BV ; 2023
    In:  Geoenergy Science and Engineering Vol. 224 ( 2023-05), p. 211592-
    Details
    In: Geoenergy Science and Engineering, Elsevier BV, Vol. 224 ( 2023-05), p. 211592-
    Type of Medium: Online Resource
    ISSN: 2949-8910
    URL: Article
    DOI: 10.1016/j.geoen.2023.211592
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 7 ( 2023-07-21), p. 2195-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 7 ( 2023-07-21), p. 2195-
    Abstract: As an important unconventional oil and gas resource, the tight gas reservoir faces many technical challenges due to its low porosity, low permeability, and strong heterogeneity. Among them, the accurate definition of effective reservoirs and ineffective reservoirs in tight gas reservoirs directly affects the formulation and adjustment of subsequent development plans. This paper proposes a reservoir effectiveness identification method based on double factors based on conventional geophysical logging data and core experimental data. The double factors considered are based on the logging response and physical parameters of the reservoir. The identification factor F1 is obtained by using the difference in the logging response values of the natural gamma logging curve, compensated density logging curve, and acoustic time difference logging curve in different reservoirs combined with mathematical operation, and the identification factor F2 is calculated by using porosity parameters combined with Archie’s formula. The validity of the reservoir can be judged by the intersection of the above double factors. This method is applied to the Shihezi Formation in the L block, and the applicability of the double factors is compared and analyzed using the traditional method. The results show that the method has strong applicability in tight gas reservoirs and that the accuracy rate reaches 96%. Compared with the direct use of the porosity lower limit method, the accuracy of the judgment is significantly improved, and the calculation is simple, easy to implement, and unaffected by mud invasion. For study areas with different geological backgrounds, the process of this method can also be used to determine the effectiveness of the reservoir. The reservoir effectiveness identification method proposed in this paper has practical engineering significance and lays a solid foundation for subsequent fluid property identification, production calculation, and development plan formulation and adjustment.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11072195
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...