Study of Fractal and Multifractal Features of Pore Structure in Tight Sandstone Reservoirs of the Permian Lucaogou Formation, Jimsar Sag, Junggar Basin, Northwest China

Han, Changcheng; Guan, Li; Dan, Shunhua; Yang, Yi; He, Xudong; Qi, Ming; Li, Geng

doi:10.1021/acsomega.2c03717

Cited by 3 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study employs the S-A model was used to decompose the composite anomalies of the PC1 and PC2 principal component combination, thereby obtaining the principal component background field and anomaly field in the study area (Figures and ). Among them, the background field obtained by S-A decomposition mainly reflects the background composition of elemental mass fractions; high-background areas may be favorable for polymetallic mineral exploration; variations in the background strength reflect the presence of elements in a favorable geological context for mineralization, and the anomaly field mainly reflects local anomalous mass molecules of elements and noise generated during data processing. , …”

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

“…Among them, the background field obtained by S-A decomposition mainly reflects the background composition of elemental mass fractions; high-background areas may be favorable for polymetallic mineral exploration; variations in the background strength reflect the presence of elements in a favorable geological context for mineralization, and the anomaly field mainly reflects local anomalous mass molecules of elements and noise generated during data processing. 23,38 Combining the geological characteristics of the study area (Figure 1) with the decomposed PC1 and PC2 principal component background fields (Figure 9b, 10b), it was found that the spatial positions of high-background areas roughly coincide with the primary ore-hosting strata in the area. Specifically, the high-background regions in the PC1 are predominantly located in the central, northern, and western parts of the study area.…”

Section: Spectrum−area Fractal Model Analysismentioning

confidence: 97%

“…Since Mandelbrot introduced the concept of “fractal geometry” in “The Fractal Geometry of Nature” in 1983, the fractal theory has gradually entered the public view . It has begun to be applied in analyzing and interpreting various complex phenomena. , With continuous research by scholars nationally and internationally, it has been discovered that elemental content exhibits scale invariance and that geological and mineralization processes often create fields characterized by self-similarity or statistical self-similarity, known as multifractal fields. ,− On the basis of this theory, various fractal and multifractal data processing models have arisen, including local singularity methods, , content-area (C-A), − concentration-volume (C-V), concentration-distance (C-D), concentration-number (C-N), spectrum-area (S-A), , Fractal projection pursuit classification (FPPC), spatially weighted singularity mapping (SWSM) and the number of feature spaces-eigenvalues (N-λ) fractal models . The aforementioned multifractal methods differ from traditional methods based on frequency distribution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

Zhao,

Yin

et al. 2024

ACS Omega

View full text Add to dashboard Cite

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu−Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu−Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Spectrum−area Fractal Model Analysismentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

Zhao,

Yin

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Dynamic contact angle θ d is one of the important characteristics of the wetting behavior of liquid on a solid surface, which can be expressed as 64 Ä where θ e is the equilibrium contact angle, ζ is the friction coefficient of contact line, C pin is the pinning coefficient, and C is the smoothing parameter. Ca is the capillary number, which can be expressed as 64 = v Ca / cl (5) where v cl is the contact line velocity. The irreducible water saturation S wc is the fraction of irreducible water in a given pore space, and more details about irreducible water saturation S wc can be found in Appendix B.…”

mentioning

confidence: 99%

A Novel Threshold Pressure Gradient Model and Its Influence on Production Simulation for Shale Oil Reservoirs

Qu,

Tang,

Lei

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

Accurate characterization of the threshold pressure gradient (TPG) in shale oil reservoirs is essential for oil production. However, oil−water two-phase flow through shale oil reservoirs with narrow pores involves thermal−hydrological−mechanical coupling, and the relationship between TPG and pore structures under multiple mechanisms has not been adequately modeled mathematically. In this paper, an analytical TPG model of shale oil reservoirs is derived first by considering the combined effects of effective stress, temperature, capillary pressure, pore structures, and boundary layer. After the established model is adequately verified by the obtainable experimental data, it is embedded into a multistage fractured horizontal well productivity model to investigate the influence of relevant parameters on shale oil production. The simulation results suggest that the TPG for the oil phase increases with the increase in effective stress, leading to the decrease in shale oil production. The cumulative oil production with dynamic TPG is much greater than that considering fixed TPG. Moreover, the wettability of the oil−water two phase (e.g., oil−water interfacial tension and equilibrium contact angle) has crucial influences on well performance; thus, changing the wettability of the oil−water two phase by chemical injection is an effective method to enhance oil recovery. The research results can provide technical support for the efficient development of shale oil reservoirs from the perspective of pore-scale two-phase nonlinear flow characteristics and macroscopic productivity evaluation.

show abstract

Multifractal analysis of the heterogeneity of nanopores in tight reservoirs based on boosting machine learning algorithms

et al. 2023

Self Cite

View full text Add to dashboard Cite

Exploring the geological factors that affect fluid flow has always been a hot topic. For tight reservoirs, the pore structure and characteristics of different lithofacies reveal the storage status of fluids in different reservoir environments. The size, connectivity, and distribution of fillers in different sedimentary environments have always posed a challenge in studying the microscopic heterogeneity. In this paper, six logging curves (gamma-ray, density, acoustic, compensated neutron, shallow resistivity, and deep resistivity) in two marker wells, namely, J1 and J2, of the Permian Lucaogou Formation in the Jimsar Basin are tested by using four reinforcement learning algorithms: LogitBoost, GBM, XGBoost, and KNN. The total percent correct of training well J2 is 96%, 96%, 96%, and 96%, and the total percent correct of validation well J1 is 75%, 68%, 72%, and 75%, respectively. Based on the lithofacies classification obtained by using reinforcement learning algorithm, micropores, mesopores, and macropores are comprehensively described by high-pressure mercury injection and low-pressure nitrogen gas adsorption tests. The multifractal theory servers for the quantitative characterization of the pore distribution heterogeneity regarding different lithofacies samples, and as observed, the higher probability measure area of the generalized fractal spectrum affects the heterogeneity of the local interval of mesopores and macropores of the estuary dam. In the micropore and mesopore, the heterogeneity of the evaporation lake showed a large variation due to the influence of the higher probability measure area, and in the mesopore and macropore, the heterogeneity of the evaporation lake was controlled by the lower probability measure area. According to the correlation analysis, the single-fractal dimension is well related to the multifractal parameters, and the individual fitting degree reaches up to 99%, which can serve for characterizing the pore size distribution uniformity. The combination of boosting machine learning and multifractal can help to better characterize the micro-heterogeneity under different sedimentary environments and different pore size distribution ranges, which is helpful in the exploration and development of oil fields.

show abstract

Study of Fractal and Multifractal Features of Pore Structure in Tight Sandstone Reservoirs of the Permian Lucaogou Formation, Jimsar Sag, Junggar Basin, Northwest China

Cited by 3 publications

References 45 publications

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia

A Novel Threshold Pressure Gradient Model and Its Influence on Production Simulation for Shale Oil Reservoirs

Multifractal analysis of the heterogeneity of nanopores in tight reservoirs based on boosting machine learning algorithms

Contact Info

Product

Resources

About