Sand-ratio distribution in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos Basin: Acoustic impedance inversion-based reservoir quality prediction

Yang

Energy Exploration & Exploitation

et al. 2023

In order to accurately predict reservoirs with similar physical properties between sandstone and surrounding rocks, this study takes Carboniferous-Permian system in southeastern Ordos Basin as an example, and puts forward a multi-attribute probabilistic neural network prediction method based on acoustic time difference compaction correction. Firstly, the time-frequency analysis method is used to divide the frequency of the original acoustic time difference curve, and the low-frequency long-trend correction of all wells is carried out based on the low-frequency components of standard wells. Then, it is frequency-divided and fused with the original high-frequency components to form new acoustic time difference data, and the long-term trend decompaction correction of acoustic time difference is completed, so that the overlapping area of acoustic time difference between reservoir and surrounding rock is reduced. Multi-attribute probabilistic neural network is an algorithm based on sampling points, before reservoir prediction, inversion parameters must be optimized to save calculation time and reduce prediction error. There is a high correlation between longitudinal velocity and lithologic change trend on the inversion profile of sound velocity. The comparison results of various inversions show that the velocity profile of multi-attribute probabilistic neural network has the highest coincidence rate and high resolution with the velocity curve of logging acoustic wave. The example shows that this method has high accuracy in predicting reservoir thickness, especially for the strata with little difference in physical properties between reservoir and surrounding rock.

Section: Applicability and Limitation Of Mpnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

Yang

Energy Exploration & Exploitation

et al. 2023

“…At the present time, geological structure interpretation has been improved with the advancement of seismic exploration and interpretation techniques as well as advanced tools that are utilized for seismic and well logs interpretation such as seismic attributes, seismic inversion, lithofacies prediction, cluster analysis approach, reservoir quality prediction, multi-attributes analysis, deep learning, and machine learning. So, multiscale seismic dip constraint geological structure interpretation copes up the issue which integrates time-frequency decomposition of seismic data and geological structure interpretation (Ali et al, 2022;Anees et al, 2022;Hussain et al, 2022;Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Tounkara

Ehsan²,

Iqbal³

et al. 2023

Front. Earth Sci.

The purpose of this research is to delineate the structures of the Lower Goru Formation, investigate fluid properties, and clarify the hydrocarbon-prone areas through seismic attributes analysis. First, the acquired data was matched by the interpretation datum. Structural analysis was done by performing horizon interpretation, fault interpretation, and contour mapping on the C-Interval of the Lower Goru Formation. Hydrocarbon zones were marked with the help of attribute analysis on seismic sections and were justified by petrophysical analysis. An integrated approach such as seismic structural interpretation, seismic attribute, spectral decomposition, and petrophysical analyses was used in current research to better understand geological structure and features. This research showed that normal faults are present in the area showing negative flower structure, horst and graben, and faults oriented north-west to south-east. The contour map shows structural inclination and faults bound closure near well locations. Variance attribute and spectral decomposition attribute were used to verify horizon lineation and fault behavior. Instantaneous amplitude and instantaneous phase attributes justify hydrocarbon bearing zones, and bright spots are present on seismic sections at C–Interval of Lower Goru Formation. Petrophysical analysis of the available wells showed a number of significant hydrocarbon zones having more than 55% of hydrocarbon saturation at the C-Interval of the Lower Goru Formation. The four possible reservoir zones in Sawan-02 well, two zones in Sawan-07 well, and three zones in Sawan-09 well are identified based on well data interpretation. Based on these analyses, the area of interest has a very good reservoir potential, structural closure, and visible bright spots. The current finding of this research will be helpful for future exploration and development of the Sawan area.

Energy Science & Engineering

“…As demand for oil and gas resources grows, an increasing number of researchers are focusing on unconventional sources, including tight sandstone oil, 1–3 gas, 4–6 shale gas, 7–9 and coalbed methane 10 . Tight sandstone gas is an essential unconventional resource, and the exploration of tight sandstone gas is widely distributed and has become a research hotspot 11–14 .…”

Section: Introductionmentioning

confidence: 99%

Pore structure of tight sandstones with differing permeability: The He 8 Member of the Middle Permian Lower Shihezi Formation, Gaoqiao area, Ordos Basin

Chen,

Zhu,

Wang

et al. 2023

Tight sandstone has strong pore heterogeneity and complex pore structure, and the pore structure of tight sandstone varies with different permeability. To study the differences in the pore structure of tight sandstone with different permeability, this study investigated the tight sandstone of the He 8 Member in the Gaoqiao area of the Ordos Basin. Factors influencing pore formation are analyzed through experiments utilizing methods, such as distinguishing casting thin sections, scanning electron microscopy, high‐pressure mercury intrusion, and low‐temperature nitrogen adsorption. The results indicate that in this area, the pores of the tight sandstone are primarily dissolution and intercrystalline pores, with occasional intergranular pores and microcracks. Type Ⅱ samples (permeability > 0.2 × 10−3 μm2) are primarily composed of dissolution and intercrystalline pores, with a few visible intergranular and microcracks. In contrast, Type Ⅰ samples (permeability < 0.2 × 10−3 μm2) mainly consist of micropores and intercrystalline pores, where microcracks are observed locally. Pore size research demonstrates that Type Ⅰ samples have pore size under 1000 nm, with peaks primarily at 4–5 nm. There are also peaks between 10–1000 nm, but without a consistent pattern. For Type Ⅱ samples with sizes smaller than 1000 nm, pore distribution is evident. Peak values when the pore size exceeds 1000 nm. Type Ⅰ samples have fewer micron‐level pores, while Type Ⅱ samples are primarily dissolution pores. The nanopores of Type Ⅰ samples consist of flat pores with good connectivity, whereas those of Type Ⅱ samples are mainly blind pores with poor connectivity. Type Ⅰ samples have larger pore fractal dimensions, more intricate pore morphology, and rougher and irregular pore throat surfaces compared to Type II samples. Hence, the sedimentary environment, diagenesis, and mineral composition affect the pore distribution in tight sandstone. The research findings highlight the variations in the pore structure of tight sandstone with varying permeability, providing crucial guidance for classifying and assessing pore structure in tight sandstone reservoirs.