The application of geostatistical inversion in shale lithofacies prediction: a case study of the Lower Silurian Longmaxi marine shale in Fuling area in the southeast Sichuan Basin, China

Liu, Xiaochen; Lu, Yangbo; Lu, Yongchao; Chen, Lei; Ma, Yiquan; Wang, Chao

doi:10.1007/s11001-017-9317-4

Cited by 9 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that during the sampling optimization process, the sampling is not directly performed in each of the classes of sample points that are generated through waveform clustering analysis or coherence analysis because the criteria for the classification cannot be quantified [36][37][38]. Therefore, the sampling optimization strategy adopted in this algorithm is to use the seismic waveforms at completed wells as the reference waveforms and to comprehensively consider both the correlation coefficients and the distances.…”

Section: Fcmcmc Algorithm-based Statistical Sampling Optimization 221...mentioning

confidence: 99%

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Dong

Gui

et al. 2023

Processes

View full text Add to dashboard Cite

To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses.

show abstract

Section: Fcmcmc Algorithm-based Statistical Sampling Optimization 221...mentioning

confidence: 99%

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Dong

Gui

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…Previous research has focused on the main controlling factors of how shale lithofacies formed in the Longmaxi Formation in the Sichuan Basin and its surrounding areas (Zhao et al, 2016a;Tang et al, 2016;Wang et al, 2016;Wu et al, 2016;Lu et al, 2017;Liu et al, 2018;Wang et al, 2020b;Wang et al, 2021b). It is generally believed that volcanic activities, terrigenous supplies, sea-level changes, and redox conditions played an important role in controlling the development of the shale lithofacies types (Zhao et al, 2016a;Wang et al, 2018).…”

Section: Main Controlling Factors Of Spatial and Temporal Variations In Shale Lithofaciesmentioning

confidence: 99%

Development Characteristics of Shale Lithofacies in the Longmaxi Formation and their Main Controlling Factors in the Changning Area, South Sichuan Basin, SW China

et al. 2021

Self Cite

View full text Add to dashboard Cite

Based on core observations, thin sections, X-ray diffraction (XRD), and seismic data, the lithofacies types in the organic-rich Longmaxi shale (Lower Silurian) in the Changning area of the southern Sichuan Basin were identified. The factors controlling the spatial variations in the shale lithofacies and the influences of the shale lithofacies on shale gas development were also analyzed. Results indicate that there are seven main types of shale lithofacies in the Long11 sub-member of the Longmaxi Formation, including siliceous shale (S-1), mixed siliceous shale (S-2), carbonate-rich siliceous shale (S-3), clay-rich siliceous shale (S-4), carbonate/siliceous shale (M-1), mixed shale (M-2), and argillaceous/siliceous shale (M-4). A vertical transition from the carbonate shale association + mixed shale association at the bottom of the sub-member to a siliceous shale association and mixed shale association + siliceous shale at the top generally appears in the Long11 sub-member. The shale lithofacies of the Long11 sub-member also laterally change from the central depression (low-lying area) to the geomorphic highland in the east and west parts of the Changning area. The spatial variations in shale lithofacies in the Long11 sub-member of the Changning area were mainly controlled by palaeogeomorphology and relative sea level. The geomorphic highland area is dominated by carbonate-rich siliceous shale and mixed siliceous shale, but the depression (low-lying area) is mainly dominated by mixed siliceous shale and argillaceous/carbonate shale.

show abstract

“…The Longmaxi Formation exhibits distinct trichotomous characteristics: the lower segment (generally 89 m thick) is composed of dark carbonaceous and siliceous mud shale; the middle segment is composed of turbidite sandstone; and the upper segment is composed of silty mudstone. The Wufeng Formation and the lower part of the Longmaxi Formation were formed from deep-water shelf deposition [28][29][30], maintained an anaerobic environment for a relatively long time, and are the main production formations in the Fuling shale gas field (Figure 2).…”

Section: Geological Settingmentioning

confidence: 99%

A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China

2021

View full text Add to dashboard Cite

Shale gas reservoir evaluation and production optimization both require geological models. However, currently, shale gas modeling remains relatively conventional and does not reflect the unique characteristics of shale gas reservoirs. Based on a case study of the Fuling shale gas reservoir in China, an integrated geological modeling workflow for shale gas reservoirs is proposed to facilitate its popularization and application and well improved quality and comparability. This workflow involves four types of models: a structure-stratigraphic model, reservoir (matrix) parameter model, natural fracture (NF) model, and hydraulic fracture (HF) model. The modeling strategies used for the four types of models vary due to the uniqueness of shale gas reservoirs. A horizontal-well lithofacies sublayer calibration-based method is employed to build the structure-stratigraphic model. The key to building the reservoir parameter model lies in the joint characterization of shale gas “sweet spots.” The NF models are built at various scales using various methods. Based on the NF models, the HF models are built by extended simulation and microseismic inversion. In the entire workflow, various types of models are built in a certain sequence and mutually constrain one another. In addition, the workflow contains and effectively integrates multisource data. Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. The workflow allows the complex process of geological modeling of shale gas reservoirs to be more systematic. It is of great significance for a dynamic analysis of reservoir development, from individual wells to the entire gas field, and for optimizing both development schemes and production systems.

show abstract

The application of geostatistical inversion in shale lithofacies prediction: a case study of the Lower Silurian Longmaxi marine shale in Fuling area in the southeast Sichuan Basin, China

Cited by 9 publications

References 42 publications

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Development Characteristics of Shale Lithofacies in the Longmaxi Formation and their Main Controlling Factors in the Changning Area, South Sichuan Basin, SW China

A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China

Contact Info

Product

Resources

About