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Understanding complex pore structures is important for evaluating tight oil reservoir performance and predicting favorable pore structure. However, quantitative characterization of pore structure in tight sandstones by combining different methods is still poorly understood. Using the Upper Triassic Yanchang Formation in Ordos Basin, China as a case study, we first introduce a new method to quantitatively characterize full-range pore-throat size distribution (PSD) through multifractal dimension analysis of integrated pressure-controlled porosimetry (PCP) and rate-controlled porosimetry (RCP). Second, we propose a technique using helium porosity and nitrogen permeability to obtain multifractal dimensions in an attempt to predict favorable pore structure in tight oil reservoirs. In the new method of obtaining full-range PSD, PCP and RCP data were merged at various positions instead of the same position for each sample. Multifractal dimension curves derived from full-range pores are divided into four segments as D₁, D₂, D₃, and D₄, corresponding to the fractal characteristics of large pores, large pore throats, small pores, and small pore throats, respectively. Among them, the fractal dimension D₂ of large pore throats and D₄ of small pore throats from the combination of PCP and RCP significantly control petrophysical properties (porosity and permeability). The multifractal dimensions obtained using porosity and permeability data input through a back-propagation (BP) neural network method show that the relatively large D₂ and the relatively small D₄ correspond to favorable pore structure and good reservoir quality. The results of this research significantly improve our understanding of complex pore characteristics and prediction of favorable pore structure in tight reservoirs, thus enhancing hydrocarbon exploration and production.

The tight oil sandstone reservoir of the Upper Triassic Yanchang Formation is one of the main targets for hydrocarbon exploration in the Ordos Basin, China. Sandstones of the Upper Triassic Yanchang Formation within lacustrine deep-water gravity-flow deposits are characterized by ultralow porosity, ultralow permeability, and complex diagenetic alterations and sedimentary lithofacies. However, the diagenetic alterations and their controls on the reservoir quality of the Chang 6 tight sandstones remain poorly understood. This study integrated a variety of techniques, such as casting thin section analysis, scanning electron microscopy (SEM), back-scattered electron imagery (BSE), X-ray diffraction (XRD), cathodoluminescence (CL) microscopy, and fluid inclusion, to investigate the lithofacies, diagenetic history, and diagenetic intensity, and also evaluate their controls on reservoir quality. The research results indicated that the Chang 6 sandstones exhibit variable intensity of diagenetic alterations among the various lithofacies, which leads to variable reservoir quality. Mechanical compaction was the most important factor reducing primary porosity. Sandstones of the Ss lithofacies (siltstone to very fine-grained sandstones with graded bedding or ripple laminations) showed the highest loss of primary porosity. Carbonate cements were commonly present in sandstones of the Sc (fine-grained and cross-bedded sandstones) and Sm (fine-grained and massive sandstones) lithofacies near the sandstone–mudstone bounding surface. Sandstones with more authigenic illite rims were able to resist compaction to some extent and were favorable to the preservation of porosity; however, abundant pore-filling illite aggregates commonly bridged pore throats, causing a significant reduction in reservoir quality. Feldspar dissolution porosity leads to a slight enhancement in reservoir quality. Intergranular pores and feldspar dissolution pores are most abundant in the sandstones away from the sandstone–mudstone bounding surface of the Sc lithofacies, resulting in the best reservoir quality, which is followed by sandstones of the Sm lithofacies. Ultimately, four evolution patterns of diagenetic alterations that formed best, moderate, or poor reservoir quality were summarized by linking diagenetic alterations to lithofacies. The research results are conducive to the evaluation of reservoir quality and to understanding the genetic mechanisms of tight sandstones, so they should facilitate hydrocarbon exploration and production in similar lacustrine deep-water gravity-flow reservoirs.

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.

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.

The point bar is one of the most important reservoirs in a meandering depositional system, and accurately building a 3D architecture model for point bars is crucial to predict hydrocarbon distribution within the reservoir. Unfortunately, we can only obtain a qualitative description about the internal architecture of the point bar due to the limited information or the low resolution of available data (such as reflection seismic data). To build a 3D prototype point bar reservoir model, we analyze the architecture of point bars by integrating high-resolution ground penetrating radar (GPR) data and modern deposition. We found that our GPR data have five main reflection patterns (GPR facies), and GPR facies can be used to relate with architectural elements (the depositional facies and geobodies within depositional facies). The concave-down GPR facies is usually related to the abandoned channel. The continuous, subhorizontal, subparallel GPR facies is commonly related with lateral-accretion sand bodies within the point bar. The multiple stacked small-scale, discontinuous reflections GPR facies is interpreted to be shale drapes within the point bar. We further analyzed the geometry parameters of the identified channels. We found that the nonsymmetric [Formula: see text] of abandoned channel near the channel axis is related to the ratio between the curvature of channel radius [Formula: see text] and channel width [Formula: see text] ([Formula: see text]). Finally, we built two 3D channel reservoir models and our models could provide useful guidance for the architecture analysis of buried meandering fluvial reservoirs.

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.

Digitate deltas consist of one or multiple separate bar fingers, which can form hydrocarbon reservoirs after burial. This paper focuses on digitate shallow-water deltas, which are commonly seen in modern and ancient deposits with similar downstream conditions. Four metrics were adopted to quantify their morphologies, including the average sinuosity, average nondimensional width (average width ratio between the bar fingers and distributary channels), nondimensional delta length (ratio between the delta length and average width of the distributary channels), and number of bar fingers. These metrics measured from 9 modern deposits and 21 Delft3D simulations exhibited wide-ranging values that were strongly affected by the upstream conditions. However, the effects of the upstream conditions remain unclear. The quantitative effects were revealed by performing Delft3D simulations: (1) the average sinuosity is proportional to the sediment cohesion and concentration and inversely proportional to the sand proportion and water discharge, and reaches equilibrium as sediment supply increases; (2) the average nondimensional width is proportional to the sediment concentration, is inversely proportional to the sediment cohesion and water discharge, has an inverse exponential relationship with the sand proportion, and is independent of the sediment supply; (3) the nondimensional delta length has a power-law relationship with the water discharge and is logarithmically related to the sediment supply; (4) the number of bar fingers is proportional to the sand proportion and sediment supply and inversely proportional to the sediment cohesion. Upstream conditions influence channel, mouth bar and levee growth, resulting in various bar finger morphologies. Empirical equations from Delft3D simulations were proven effective in examinations of nine modern deposits and applied to help predict the distribution of a digitate shallow-water delta reservoir. This work improves the fundamental understanding of the upstream controls in digitate shallow-water deltas and may help enhance the inter-well prediction of paralic reservoirs.

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.

The physical properties of tight shale reservoirs have always been a hot topic of discussion, and the tuff reservoirs have also attracted more and more attention. Log predicting of the porosity is also important for the exploration and development of tight oil. Tight oil resources have been found in tight shale and tuff reservoirs in the Chang 7 Member of the Upper Triassic Yanchang Formation in the Ordos Basin, China. However, the logging method that effectively predicting porosity of the Chang 7 Member is lacking, and few people have discussed how to optimize the log interpretation methods. In this study, the characteristics of shale and tuff reservoirs were summarized by microscope and scanning electron microscope observation, X-ray diffraction, pore and permeability experiment, and the logging data. In order to find the optimal method of porosity prediction, four methods were performed: multiple regression fitting, multi-component volume model, porosity logging formula, and back propagation neural network method. The “Zhangjiatan Shale” of the Chang 7 Member is characterized by abundant sandy lamina and tuff lamina, compared with the tuff reservoir, the enrichment of pyrite (average 22.7 wt%) and total organic matter (>6%) increase the content of intercrystalline pores and organic matter related pores, the logging response is characterized by high natural gamma ray, low spontaneous potential and high acoustic. The tuff reservoir is mainly composed of vitric tuff and crystal-vitric tuff, hence, the rich in intergranular pore and micro-fracture can be explained by the dissolution and devitrification of vitric and crystal fragments, and the logging characteristic is opposite to that of the “Zhangjiatan Shale”. Four methods for predicting porosity are established. First, appropriate logging parameters are selected for regression fitting in the “Zhangjiatan Shale” (e.g., gamma ray, deep lateral logging resistivity, density and spontaneous potential) and tuff reservoir (e.g., acoustic, density, compensated neutron logging). Then, the rock multi-component volume model was established as rock skeleton, clay or tuff matter, organic matter, and pores. The logging parameters of the skeleton, clay and organic matter were obtained by intersection graphs, which were applied in the improved porosity logging formulas. The effect of organic matter on logging was considered into the traditional porosity prediction formula, a third prediction method was established. Finally, the BP method was introduced into the porosity prediction work, and the neural network parameters were adjusted according to the log and porosity data. The learning rate and the learning accuracy were set as 0.1 and 10−6 in the study area. The error analysis between the result predicted and measured shows that the multi-component volume model is suitable for the “Zhangjiatan Shale” (the correlative coefficient R>80%), while the back propagation neural network is the best method for the tuff reservoir (the correlative coefficient R>90%).

The geological and geophysical characterization of hydrocarbon-bearing sandstones of fluvial origin is a challenging task. Channel sandbodies occurring at different stratigraphic levels (i.e., in a reservoir interval of interest as well as in overlying and underlying stratigraphic intervals) but overlapping in planview usually cause significant seismic interference due to limitations in seismic resolution: this can produce significant error in the prediction of sand location and thickness using seismic attributes. To mitigate the effect of seismic interferences by zones neighboring a target reservoir interval, a new method is proposed that combines multiple seismic attributes of the target interval and of its interfering neighboring zones, implemented by a supervised machine learning algorithm using support vector regression (SVR). Since the thickness of neighboring intervals causing seismic interference has a constant value of a quarter of a wavelength (1/4 λ), the stratal slice corresponding with the top horizon of the target interval is taken as the base of a window of 1/4 λ to calculate seismic attributes for the overlying zone; similarly, the stratal slice corresponding with the bottom horizon is taken as the top of a window of 1/4 λ to calculate seismic attributes for the underlying zone. The proposed method was applied to a subsurface dataset (including a 3D seismic dataset and 255 wells) of the Chengdao oilfield, in the Bohai Bay Basin (China). The interval of interest is located in the Neogene Guantao Formation, whose successions are interpreted as fluvial in origin. This application demonstrates how the proposed method results in remarkably improved sandstone thickness prediction, and how consideration of multiple attributes further improves the accuracy of predicted values of sandstone thickness.

Fluid mobility is one of the most important factors in evaluating the potential for recovering tight oil. However, quantitative effects of multiple factors that influence movable fluid saturation in different pore-throat combinations and their relationships with lithofacies in tight sandstones, especially for lacustrine deep-water gravity-flow deposits, remain controversial due to the strong heterogeneity and complex pore structure of tight reservoirs. Core samples obtained from the Upper Triassic Yanchang Formation in southern Ordos Basin were evaluated by using a variety of techniques, including nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), X-ray diffraction (XRD), pressure-controlled porosimetry (PCP), rate-controlled porosimetry (RCP), impregnated thin sections, and helium porosity and nitrogen permeability measurements. Based on NMR T2 distributions under water-saturated and centrifugal conditions, four types of pore-throat combinations are identified by using the relaxation time thresholds Ts1 and Ts2 from NMR: large intergranular pore-dominated pore-throat combinations (LIP), small intergranular pore-dominated pore-throat combinations (SIP), intragranular pore-dominated pore-throat combinations (IAP), and micropore-dominated pore-throat combinations (MP). Among them, SIP is the dominant pore space favorable for fluid flow. Movable fluid saturation in SIP pore space varies among different lithofacies. The best fluid mobility commonly occurs in fine-grained, cross-bedded sandstones (Sc), whereas the worst fluid mobility usually appears in siltstones to very fine-grained sandstones (Ss). The quantitative effects of a total of 41 factors that influence movable fluid saturation of different pore-throat combinations were investigated through the analysis of the Pearson correlation matrix. The results demonstrate that porosity and permeability mainly affect the movable fluid saturation in the pore space of SIP and MP. In addition, maximum mercury intrusion saturation from PCP (Smax) and tortuosity (λ) are the critical pore structure parameters affecting the movable fluid saturation in the pore space of SIP and MP, whereas the average pore throat radius ratio (η) is the critical factor affecting the movable fluid saturation in LIP pore space. Overall, movable fluid saturation in different pore-throat combinations is characterized mainly by different microscopic pore structure parameters. A general pore network model for different lithofacies with different fluid mobility is established to facilitate assessment of the heterogeneity of tight sandstones and to further guide hydrocarbon exploration and development in similar lacustrine deep-water depositional settings.

Clastic sedimentary successions are subject to variable amounts of compaction, which causes a decrease in both the thickness and porosity of sand-rich depositional units. Methods for estimating the degree of sediment compaction are needed for characterizing changes in the geometry and petrophysical properties of depositional elements in relation to their burial history. Conventional methods for estimating compaction of rock successions through the application of several empirical equations return estimations whose uncertainties can be significant, and integrative approaches that can produce reliable estimations are therefore desirable. To this end, a new method is proposed here for the estimation of the degree of compaction of sandbodies in ancient channelized fluvial successions. For outcropping fluvial successions, the compacted geometry of channel-fill margins cut into non-cohesive deposits can be measured, whereas the decompacted angle of repose of the material originally forming the channel banks can be estimated experimentally. Sediment compaction can therefore be estimated by comparing the observed geometry of the uppermost part of a channel-fill margin with the angle of repose of the non-cohesive bank material. The proposed method has been applied to three different sand-prone fluvial successions seen in outcrop, for the purposes of (i) illustrating the approach, and (ii) testing it through a comparison of its estimations against results produced by a conventional method based on thin-section observations. The comparison demonstrates that the two methods yield similar results, highlighting how the proposed approach can be readily applied to the assessment of compaction in clastic successions, for scopes of both pure and applied geological research.

Taking common heavy oil reservoir as an example, we characterized spatial structure characteristics of delta front and analyzed waterflooding characteristics of underwater distributary channel sand, mouth bar sand, and beach bar sand, finally cleared the fact that sandstone architecture has influence on waterflooding characteristics in common heavy oil reservoir. The research results show that spatial structure characteristics of delta front are complicated: vertically, single sand bodies have independent, superposed, and cutting-stacked contacts with each other. Sandstone architecture has great influence on waterflooding development: Underwater distributary channel sand has positive rhythm characteristics, and mouth bar sand has weak inverted rhythm characteristics with low permeability contrast value as well as beach bar sand has homogeneous rhythm characteristics. Therefore, at the process of waterflooding development, because of the influence of gravity and high viscosity of heavy oil, the bottom part of underwater distributary channel and the bottom part of most mouth bar sands are priorly waterflooded, while beach bar sand is waterflooded uniformly; long-term waterflooding and high viscosity characteristics of heavy oil will lead to formation of dominant water flowing channel in sand inside and cause remaining oil accumulate at the top of sands, which are the main target for late tapping.

The natural gas reservoir beds of different areas in China can be divided into three kinds, clastic natural gas reservoir bed, carbonate natural gas reservoir bed and special natural gas reservoir bed. They have different combination patterns controlled by deposition, diagenesis and tectonism. Our analysis indicates that the natural gas reservoirs are mainly distributed in the Precambrian, Palaeozoic, Mesozoic, and Tertiary-Quaternary. Craton basin, foreland basin and intracontinental rift basin which contain most of natural gas in China have special geological features and favorable accumulation conditions, and will be important exploration areas in the future.

The studies on fluvial reservoir architecture are mainly aimed at outcrop and modern deposition, but rarely at the subsurface reservoir, so there are few effective methods to predict the distribution of subsurface reservoir architectures. In this paper, taking the meandering river reservoir of Guantao formation Gudao Oilfield, Jiyang depression, Baohai Gulf Basin, East China as an example, the architectural modeling method of complex meandering belt reservoir is proposed, that is hierarchy constraint, pattern fitting and multi-dimensional interaction. Architectures of meandering river reservoir can be divided into three hierarchies: meandering channel sandbody, point bar and lateral accretion body. Different hierarchies of the quantitative architecture pattern are fitted to subsurface well data (including dynamic monitoring data) in different hierarchies through one-dimensional hole, 2D profiles and plane and 3D space, which are verified by each other. And then 3D model in different hierarchies is established. At the same time, the quantificational relationship between width of active river and the scale of point bar is set up, and the scale of lateral accretion sand body and shale beddings is confirmed with horizontal well data. The study not only has significant meaning on the development of geology, but also can improve the oilfield exploitation greatly.