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Accurately characterizing meander belts and point bars in fluvial reservoirs is significant for reducing drilling risks and improving the recovery of oil and gas. For this goal, we developed a new seismic inversion method that combines spectral decomposition and genetic inversion for improving vertical and horizontal resolution of seismic interpretation and readjusting tuning effects. This method takes advantage of multiple spectral-decomposition seismic cubes. Seismic traces, integrated traces (90°-phase data), sweetness, and root-mean-square volume attributes were input to consider information from a range of seismic data types. Tests using a sample conceptual model and a real-world dataset demonstrate that the proposed method could achieve satisfactory results, improve the resolution of inversions, and readjust tuning effects. The real dataset is a case study from the Chengdao Oilfield in the Bohai Bay basin in China and the Neogene Guantao formation is the interval of interest. Our study area covers approximately 85 km2, and the dataset used includes mainly 441 wells and a post-stack 3D seismic cube with a dominant frequency of 40 Hz. In the spectral-decomposition genetic inversion results, distributions of inversion values were in relatively good accord with the well logs, and several thin adjacent sandstone bodies that could not be distinguished in the conventional genetic inversion results could be distinguished distinctly. We also proposed a workflow for the characterization of fluvial meander belts and point bars by integrating inversion results and quantitative depositional models. According to the surface attributes and red-green-blue blending maps of multiple stratal slices calculated from the inversion cube, one main meander belt, eight narrow channel fills, and several overbank sandstone bodies were accurately imaged; most of these features were confirmed from well observations. Furthermore, 15 point bars and related abandoned channels were characterized in the main meander belt. The proposed inversion method can improve the resolution of seismic interpretations and the undertaken characterization of meander belts and point bars could provide an example for other fields with a similar dataset. • A new method of combining spectral decomposition and genetic inversion is proposed. • Spectral-decomposed genetic inversion can effectively improve resolution. • RGB blending of stratal slices is an effective method to display inversion results. • Point bars were characterized by a joint quantitative model and inversion results. [ABSTRACT FROM AUTHOR]

Abstract Defining the boundaries, thicknesses and sedimentary facies of fluvial reservoirs (sand bodies) is critical for predicting hydrocarbon volumes, designing schemes for petroleum exploration and development and improving oil recovery. Most reservoirs contain thick and thin sand bodies at the same intervals, while the amplitude values of seismic data usually highlight sand bodies near the 1/4 wavelength for the tuning phenomena. Hence, the application of spectral decomposition to seismic attributes and the combination of multiple frequency-decomposed (spectral-decomposed) seismic attributes have gained increasing attention for the readjustment of tuning thickness to predict sand bodies of various thicknesses. However, the popular method of red-green-blue blending is a simple linear combination of three frequency-decomposed seismic attributes that qualitatively analyzes the sand thickness without well-log interpretation. This research proposes machine learning fusion as a new nonlinear method for fusing high-, middle-, and low-frequency seismic attributes. This method uses machine learning to link well-log interpretation and multiple-frequency seismic attributes for the quantitative prediction of sand thickness, which is important for development work in a mature field. Test results of the conceptual model and the real case indicate that the predicted sand thickness after fusing multiple frequency-decomposed seismic attributes is approximately in line with the actual thickness (correlations between 80 and 90%). Combined with the coherence attribute and the red-green-blue blending results, the distributions and histories of sedimentary facies are analyzed based on the predicted sand thickness and well data. The results suggest that the proposed method can effectively readjust the tuning thickness and improve the resolution of seismic interpretation. This method is a potentially effective technique to characterize the sand thickness and sedimentary facies in other fields using a similar geological setting and dataset. Highlights • Fusing multiple-frequency attributes with machine learning is first proposed. • The new method distinctly readjusts tuning effects and improves seismic resolution. • The sand thickness in fluvial meandering reservoirs is quantitatively predicted. • The stacked meandering channels are accurately described. [ABSTRACT FROM AUTHOR]

Abstract Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a Support Vector Machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32–6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data. Highlights • Fused SDSAs by SVM algorithms can better depict the boundaries and thickness of sands. • Response patterns of conceptual forward models can predict stacked sands quantitatively. • Optimization of forward seismic models increase the reliability of sand prediction. • The method proposed characterises the distribution of sedimentary facies accurately. [ABSTRACT FROM AUTHOR]

The main target for hydrocarbon exploration in the western Sichuan Basin in China is the tight gas sandstone reservoir of the Upper Triassic Xujiahe Formation. Sandstones of the Xujiahe Formation are characterized as tight with ultralow porosity, ultralow permeability, and intensive heterogeneity. In this study, various techniques, such as scanning electron microscopy, X-ray diffraction, cathodoluminescence microscopy, casting thin-section, and fluid inclusion thermometry, were employed to investigate the composition, sedimentary facies, diagenesis history, diagenetic intensity, natural fracture characteristics, and their impact on reservoir quality. The results showed that subaqueous distributary channels and mouth bars with low rock fragment content are favorable for pore preservation and secondary pore development. Except for the medium to strong dissolution intensity, chlorite rim growth is also important for the development of high quality reservoirs. Intraformational shear fractures and horizontal shear fractures associated with tectonic movement increase pore space and improve the capability of transmitting fluids. Differences in gas productivity could be attributed to differences in reservoir quality, which were controlled by the sedimentary environment, diagenetic intensity, and fracture characteristics. A significantly positive correlation was found between total porosity and gas productivity of the tight sandstones. The high density fracture distribution was found owing to the fault system and structural deformation zone, which resulted in high gas productivity. The research results were successfully applied to improve production efficiency in the Xujiahe tight gas sandstones, and could be applicable to other similar fields involving tight gas. [ABSTRACT FROM AUTHOR]