Reservoir Prediction Research Articles

Full-time domain matching pursuit and empirical mode decomposition based sparse fixed-point seismic inversion

Abstract Sparse seismic inversion is wildly utilized for reservoir prediction and resolution improvement. Matching pursuit (MP) is an effective algorithm for solving L0-norm and obtaining sparse inversion results. Sparse seismic inversion based on MP (MPSI) can estimate the sparse parameters of subsurface from observations by controlling the iterations and threshold. However, the low convergence and stability limit the application of MPSI. To accelerate the convergence of MP, the full-time domain matching pursuit (FTMP) algorithm is first proposed. The seismic inversion based on FTMP can realize the multi-point inversion simultaneously instead of searching the inversion results one by one, which is the process of MPSI. Also, the prior model constraint is then involved in the objective function to improve the stability and the layer-boundary fidelity of the inversion results. Furthermore, the empirical mode decomposition (EMD) algorithm is introduced into inversion framework to recover the features of the seismic signal from the noisy seismic signal. The fixed-point (FP) algorithm is adopted to solve the objective function in this study. The optimal inversion results can be estimated after finite iterations by the FP algorithm. Combining the FTMP sparse seismic inversion framework, prior model constraint, EMD and FP algorithms, a complete sparse seismic inversion method named the full-time domain matching pursuit-based sparse fixed-point seismic inversion is ultimately proposed. The synthetic and field examples are utilized to demonstrate the stability and practicality of this approach. Compared with MPSI, the inversion results of the proposed method have higher resolution and fidelity of the layer-boundary.

Productivity Prediction of Fractured-Vuggy Reservoirs under Time-Varied Flow Rates and Bottom Hole Flow Pressures

Fracture-vuggy oil reservoirs contain micrometer-sized dissolved secondary pores, fractures, and meter-sized karst caves. Generally, the researches on the flow mechanism of fractured-vuggy reservoirs are based on the assumption of karst cave-fracture-matrix triple medium that gives a partial differential equation which the pressure satisfies. This triple medium flow equation lays the basics for the well testing theory of fractured-vuggy reservoirs. However, the triple medium equation cannot reflect the actual existence of the karst cave volume in geology. Recently, we proposed a theory of fracture-cave seepage flow coupled with vug pressure wave which takes the vug size into consideration and thus can better describe the fracture-vuggy reservoirs. On the basis of this theory, this work combines the change of formation pressure with time, Duhamel’s theorem, and well testing theory to obtain the mathematical expression of time-varied bottom hole pressure and flow rate in Laplace space. Then, the dynamic inflow performance relationship (IPR) curve of fractured-vuggy reservoir is obtained by inverse Laplace transformations. This dynamic IPR curve can predict the productivity for fractured-vuggy reservoirs using parameters from well test interpretations of pressure recovery. The correctness of this productivity prediction method is verified by filed fractured-vuggy oil well. This productivity prediction method not only utilizes and expands the application of well test interpretation data but also shortens the test time and reduces test costs, which is important for oil development.

Characteristics of Carboniferous Volcanic Reservoirs in Beisantai Oilfield, Junggar Basin

In recent years, petroleum exploration in the Carboniferous volcanic rock reservoirs in the Junggar Basin has been the focus of important petroleum energy development in western China. The lithologic identification of volcanic rock reservoirs seriously restricts the accuracy of reservoir prediction and affects the success rate of oil exploration. Different types of volcanic rocks have different petrological characteristics and mineral assemblages, especially affected by the depositional environment. The volcanic rocks in different regions have their own uniqueness. This paper takes the Carboniferous volcanic reservoirs in Xiquan block, Beisantai Oilfield, Junggar Basin as the research target. Through a large number of core observations, casting slices, scanning electron microscopy, and X-ray diffraction methods, the Carboniferous volcanic rocks are analyzed. The petrology, pore characteristics, physical properties, and diagenetic evolution history of the reservoir are analyzed. The study shows that the volcanic facies in the Xiquan block can be divided into explosive facies, overflow facies, and volcanic sedimentary facies, among which the explosive facies is subdivided into empty subfacies (volcanic breccia-breccia tuff combination) and thermal base wave subfacies (tuff). The lithology of the reservoir is pyroclastic rock and volcanic lava, belonging to medium-porous and ultralow permeability reservoirs, and the storage space can be divided into three types: primary pores, secondary pores, and fractures. The lithology of key exploration is breccia tuff, followed by breccia tuff and volcanic breccia.

A Flownet-Based Method for History Matching and Production Prediction of Shale or Tight Reservoirs with Fracturing Treatment

Summary Based on a newly developed physics-based data-driven model FlowNet, this paper presents an effective method for history matching and production prediction of fractured shale or tight reservoirs without any prior information about fracture geometry. In this method, four types of well nodes including fracturing cluster nodes, fracture nodes, stimulated reservoir volume (SRV) nodes, and matrix nodes are allocated in the reservoir. Then, the reservoir model is simplified as a flow network composed of some 1D connection elements between these nodes. Some grids are divided on each connection element, and the grids on the same connection element are of equal width and permeability. Subsequently, a fully implicit nonlinear solver is used to solve flow equations in this FlowNet grid system to obtain pressure, phase saturation, and production rates, etc. Efficient history-matching procedure based on the FlowNet model of the fractured reservoir is used to determine the parameters of connection elements, and then fast production prediction can be conducted. Five numerical examples including single-well depletion, waterflooding development with natural fractures, multiple-well interference, three-phase flow, and an actual waterflooding field case validate that this presented FlowNet-based method can achieve good history matching and production prediction for various flow problems in shale or tight reservoirs with fracturing treatment, and the history-matched transmissibility and volume of connection elements can reflect the existence of high-conductivity fractures.

Pore structure analysis and permeability prediction of shale oil reservoirs with HPMI and NMR: A case study of the Permian Lucaogou Formation in the Jimsar Sag, Junggar Basin, NW China

The pore structure characteristics and formation permeability are important reservoir information for the development of shale oil. The pore structures of the shale oil formation in the Lucaogou Formation in the Jimsar Sag are characterized with high-pressure mercury injection (HPMI), nuclear magnetic resonance (NMR), and fractal theory. The capillary bundle model, geometric model, and wetting phase model are used to obtain the fractal dimension of the capillary pressure curve. Among them, Df1 from the capillary bundle model is between 2.1937 and 3.4131, Df2 from the geometric model is within a range of 1.0414–3.575, and Df3 from the wetting phase model is from 0.5691 to 2.7047. For NMR spectra, the pore space is divided into micropores, mesopores, and macropores based on the peak value of the NMR T2 spectra, and the fractal dimension of different pore types is calculated, where Dfmi is from 1.564 to 2.301, Dfme is in a range from 2.082 to 2.941, and Dfma is between 2.831 and 2.991. The relationships between the fractal dimension and petrophysical parameters are analyzed, and the results indicate that among the three types of models, the fractal dimension of the capillary bundle model has the highest correlation with the core petrophysical parameters. Therefore, it is recommended to use the capillary bundle model to evaluate the fractal characteristics of pore structures. In particular, the fractal dimension is positively correlated with the throat coefficient and negatively correlated with the size of the pore throat. In contrast, the fractal dimension of NMR has a poor correlation with core petrophysical parameters. There is no obvious correlation between the fractal dimension of micropores and petrophysical parameters, while the fractal dimension of mesopores and macropores is only correlated with the throat-sorting coefficient, and the correlation coefficient is less than 0.15. Thus, the fractal dimension from NMR data is not suitable for studying the fractal characteristics of shale oil cores in the Lucaogou Formation. In addition, we predicted the permeability of shale cores using the Swanson model, Pittman model, and Winland model. The permeability prediction results show that the Swanson model has the greatest accuracy, followed by the Winland model. Among them, the accuracy index (ACI) of the Swanson model is 0.901, and the ACI of the Winland model is between 0.483 and 0.897. The permeability prediction by the Pittman model is poorest among these three models.

Frequency information extraction based on time-frequency ridges for characterizing thin sand bodies

Abstract Time-frequency analysis (TFA) is a widely used reservoir prediction technology. During the identification of sand bodies using TFA, abnormally high values in the high-frequency portion of the spectrum usually indicate the presence of thin sand bodies. However, owing to the complexity of the depositional environment, the spatial distribution of thin sand bodies is often variable, and multiple phases of sand bodies—superimposed on each other—can occur with uneven thicknesses. A frequency information extraction technique based on time-frequency ridges (TFRs) is proposed in this study to identify stable TFRs in the time-frequency spectrum of a rearranged smooth pseudo-Wigner–Ville distribution with high energy aggregation, as well as to extract the main- and high-frequency energy contents relative to them. To enhance the ability of these energy components to detect thin sand bodies, a frequency-energy weighting factor is computed. By performing the aforementioned steps, information about each frequency band can be obtained and a comprehensive depiction of sand bodies with different thicknesses achieved. It is concluded that by using the proposed method thin sand bodies such as small riverbeds can be effectively characterized using seismic data.

A Carbonate Reservoir Prediction Method Based on Deep Learning and Multiparameter Joint Inversion

Deep-water carbonate reservoirs are currently the focus of global oil and gas production activities. The characterization of strongly heterogeneous carbonate reservoirs, especially the prediction of fluids in deep-water presalt carbonate reservoirs, exposes difficulties in reservoir inversion due to their complex structures and weak seismic signals. Therefore, a multiparameter joint inversion method is proposed to comprehensively utilize the information of different seismic angle gathers and the simultaneous inversion of multiple seismic parameters. Compared with the commonly used simultaneous constrained sparse-pulse inversion method, the multiparameter joint inversion method can characterize thinner layers that are consistent with data and can obtain higher-resolution presalt reservoir results. Based on the results of multiparameter joint inversion, in this paper, we further integrate the long short-term memory network algorithm to predict the porosity of presalt reef reservoirs. Compared with a fully connected neural network based on the backpropagation algorithm, the porosity results are in better agreement with the new log porosity curves, with the average porosity of the four wells increasing from 89.48% to 97.76%. The results show that the method, which is based on deep learning and multiparameter joint inversion, can more accurately identify porosity and has good application prospects in the prediction of carbonate reservoirs with complex structures.

3D Geomechanical Modeling and Prediction of Fractures in the Bozi Ultra-Deep Gas Field in the Kuqa Depression of Tarim Basin

In order to clarify the fracture distribution characteristics of the Bozi gas field in the Kuqa Depression, based on the statistics of fracture parameters from core data and imaging logging, the three-dimensional (3D) structural model of the complex thrust structure is established by using the voxel-based morphometry (VBM) structural framework modeling technology; the 3D heterogeneous rock mechanics field in the study area is constructed by well-to-seismic integration. The relationship between in situ stress field and fracture parameters is established under the consideration of rock fracture criterion, and fracture prediction of the Cretaceous reservoir in the Bozi gas field is carried out with the finite element numerical simulation method. Considering the influence of in situ stress field on fracture parameters, fracture activity is analyzed. The results show that the Cretaceous reservoirs in the Bozi gas field generally develop fractures, most of which are high-angle shear fractures of tectonic origin and are semi-filled or unfilled. The fracture distribution of the Bozi gas field is obviously controlled by local structures such as folds and faults. From north to south, the fracture development gradually weakens, and the fracture density of Bozi 104 and Bozi 102 fault blocks in the north is the highest. The fracture in the northern fault block of the Bozi gas field shows high activity, while that in the southern fault block shows low activity. The fractures in the higher structural parts of most fault blocks show high activity.

Application of Far-Gather Seismic Attributes in Suppressing the Interference of Coal Beds in Reservoir Prediction

The sandstone reservoir of the Pinghu Formation in the Xihu Depression, East China Sea is characterized by great depth, small thickness, radical facies change and a widespread coal bed. It is difficult to describe the reservoir accurately using conventional reservoir prediction methods. In order to analyze the influence of coal-bearing strata on the prediction of the mid-low thickness sandstone reservoir, the seismic response of different sandstone–coal stratigraphic assemblages was simulated by seismic forward modeling. The modeling result indicates that the post-stack seismic response is dominated by coal bed, whereas the response of sandstone can hardly be recognized. In contrast, the difference between the pre-stack AVO (amplitude versus offset) response characteristics of coal seams and gas-bearing sandstones has been clarified based on the statistics pertaining to AVO characteristics of drilled wells. Therefore, we propose a method to reduce the interference of coal beds in sandstone reservoir prediction using far-gather seismic information. This method has significantly improved the accuracy of reservoir prediction and sand description in sand–coal coupled environments and has been applied successfully in the exploration of coal-rich strata in the Pingbei slope belt, Xihu Depression.

Sedimentary facies characterization of forced regression in the Pearl River Mouth basin

Abstract The Miocene Zhujiang Formation is the key horizon for oil and gas exploration in the Pearl River Mouth basin of northern South China Sea. With the help of core observation, seismic attributes and various analytical data, the sedimentary facies marks, distribution of sedimentary facies and depositional model of forced regression in the Miocene Zhujiang Formation of the Pearl River Mouth basin, northern South China Sea, have been studied. Forced regressive deposits were formed during the period when relative sea level ranged from highstand to lowstand and the sediments were forced to undergo progradation so that five sets of foreset delta deposits are developed in turn. In the early stage of forced regression, the normal delta where the delta plain, delta front and prodelta are not absent mainly developed. In the later stage of forced regression, the shelf edge delta with only the delta front and the prodelta, the longshore bar along the shelf break and the turbidite fan in the deep water of the slope area were developed. The favorable reservoir of forced regressive deposits are located near the upper boundary of the falling stage systems tract and the basal surface of forced regression, and they are the sand bodies of shelf edge delta, longshore bar and turbidite fan. The research results may provide guidance for reservoir prediction.

Study on Sedimentary Facies and Reservoir Characteristics of Paleogene Sandstone in Yingmaili Block, Tarim Basin

Block Yingmai 7 is structurally located in the western segment of the southern margin of the Kuqa Depression in the Tarim Basin. In the foreland basin, huge continental Mesozoic and Cenozoic strata have been deposited. In recent years, the Paleogene bottom sandstone section has been the main oil and gas exploration horizon. In order to further improve the oil and gas exploration effect of the Paleogene bottom sandstone in the study area and improve the accuracy of reservoir prediction, based on the related theory of sedimentology and petrology, this paper analyzes and studies the cores obtained by drilling in the Paleogene through laboratory core analysis experiments. The study shows that the Paleogene bottom sandstone is a braided river delta sedimentary system, and the sedimentary microfacies are mainly underwater distributary channels. The sandstone type is mainly light gray lithic feldspar fine sandstone, the sandstone debris particle sorting degree is medium-good, and the roundness is sub-edge-sub-circular. The microscopic characteristics of the reservoir are large pore throat radius, low displacement pressure, mainly intergranular pores, and good pore connectivity. It is a thin-layered mesoporous, medium-permeable, large-throat, and strongly heterogeneous reservoir. Finally, a classification evaluation table of reservoirs in block Yingmai 7 is established, and it is considered that the reservoirs of types I and II are enriched areas of oil and gas resources. The research results provide evidence for the prediction of oil and gas reservoirs.

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors than sandstone reservoirs, so the natural fractures they often incorporate have positive impacts on resource recovery and fluid production rates. Quantifying effective permeability, incorporating contributions from pores and fractures, is therefore essential in the reservoir characterization and flow-regime modelling of carbonate reservoirs. This research applies a robust machine-learning forecasting model to predict permeability (K) for heterogeneous carbonate gas condensate reservoirs. A 212-point dataset from six gas-condensate carbonate reservoirs (Russia and Iran) is compiled. The input variables considered are porosity (Φ, %), specific surface area (Sp, 1/cm) and irreducible water saturation (Swir, %). These variables are assessed using four machine learning models: group method of data handling (GMDH), polynomial regression (PR), support vector machine (SVR), and decision tree (DT) to predict permeability. The GMDH algorithm, a polynomial neural network with a customized architecture is developed, such that it displays increased prediction accuracy and improved learning capabilities. All four models developed in this study substantially improve upon K predictions derived from established empirical correlations. The GMDH model also outperforms the other models in respect of K prediction accuracy using Φ, Swir, and Sp as input variables. It achieves permeability prediction accuracy for the multi-field dataset evaluated with a root mean squared error (RMSE) and coefficient of determination (R2) for the training and testing of the best model (GMDH) of RMSE = 9.2 mD and R2 = 0.9988; RMSE = 0.4 mD and R2 = 0.9972, respectively. The model can be readily adapted for application to other field datasets to estimate K from limited well-log and/or core data.

Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: A case study

Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: A case study

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained. Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.04

Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

The tight sandstone reservoirs of the Xujiahe Formation of the Western Sichuan Basin Depression typically exhibit characteristics such as low porosity, low permeability, and strong heterogeneity, resulting in weak seismic response differences between gas and water. In present study, we developed an unsupervised learning gas reservoir prediction method using a self-organizing neural network (SOM) to identify and predict tight-sand gas reservoirs in areas with few or no wells. First, we extracted seismic attributes data and obtained multi-component seismic composite attributes. Then, the multi-component composite attributes were fed into the SOM network for training, obtaining the gas reservoir prediction results. Simultaneously, the designed SOM prediction scheme was also applied to PP-wave and PS-wave seismic data respectively for gas reservoir identification and prediction. The prediction results of the multi-component composite attributes exhibited good correspondence with available gas drilling information, indicating their unique merits in resolving issues pertaining to the deep tight gas sandstone reservoir identification. Finally, based on geological data and drilling information, we evaluated the effectiveness of the prediction results of proposed methods, and predicted favorable exploration areas. Effective implementation contributes substantially to tight-sand gas reservoir identification and prediction in less or even no well area. • Unsupervised learning-based self-organizing neural network. • Delineate a tight-sand gas reservoir using multi-component seismic data. • Gas reservoir identification and prediction in few wells or even no well area.

Connection element method for reservoir numerical simulation

<p indent=0mm>Abstract: The reservoir numerical simulation presently encounters problems based on the fine grid. These include poor convergence and difficult history matching. In practical application, the coarse grid often leads to the destruction of the flow structure and the significant reduction of the prediction accuracy. This paper proposes a new reservoir numerical simulation method different from the traditional grid-based methods. The proposed method discretizes the reservoir calculation domain into a series of connection elements from the flow perspective. According to the physical properties of nodes, the support domain and the moving least squares are used to estimate the Laplace operator of pressure and provide the calculation method of the attribute parameters reflecting the flow ability (connection transmissibility) and mass basis (connection volume) of the connection element. Taking the connection element as the object, the flow flux in the element is obtained by solving the node pressure equation. The transport equation about saturation is also solved by using a semianalytical method along the connection element to obtain the production dynamics. The path tracking algorithm is introduced to quantitatively represent the flow interaction and connectivity between the source and sink nodes. Finally, a reservoir numerical simulation method based on the connection element system, which is referred to herein as the connection element method (CEM), is introduced. Practical applications show that the CEM can realize the fast prediction and history matching of the reservoir production performance and identification of the injection-production connectivity. The conceptual example also verifies that in the case of the same node (grid) distribution, the CEM shows better calculation accuracy and efficiency compared with the traditional finite difference method. At the same time, the node allocation in the CEM is more flexible and can ensure the flow path integrity under fewer nodes to improve the prediction accuracy. It also supports the parallel computing of transport equations on connection elements. In conclusion, the CEM can better meet the requirements of the rapid prediction of actual large-scale reservoirs and provide a new idea for reservoir numerical simulation with complex geometric characteristics, such as fracture, fracture-cavity type, and complex boundary.

Unconventional natural gas accumulation system

Unlike conventional natural gas accumulations, unconventional ones have obvious particularity in reservoir forming conditions, enrichment mechanism, distribution modes, prediction method, and exploration technology. Analyzing the reservoir forming process and main controlling factors of unconventional natural gas accumulations under the guide of the hydrocarbon accumulation system theory is of great significance for unconventional natural gas reservoir evaluation and prediction. Based on the reservoir forming mechanism and distribution mode, the unconventional natural gas accumulation systems are divided into six types, including shale gas, coalbed methane, tight carbonate gas, tight sandstone gas, water-soluble gas, and gas hydrate. According to the concept of “element–function–structure” in the hydrocarbon accumulation system theory and the relationship of “gas source rock–migration system–accumulations distribution”, the unconventional gas accumulation system is divided into six “source–location” types. The characteristics of the other five types of unconventional gas accumulation systems except gas hydrate are analyzed. The following results were obtained. (1) Shale gas, coalbed methane and tight carbonate gas belong to an intra-source type unconventional gas accumulation system, characterized by intense gas supply capacity, lack of either a transport system or secondary migration, integration of source, reservoir and caprock, in-situ accumulation, and so on. (2) Tight sandstone gas and water-soluble gas belong to a marginal unconventional gas accumulation system of source rock, characterized by diverse gas origin, near-source accumulation in adjacent layers of source rocks, internal migration within reservoirs, and so on. (3) Basin tectonic evolution, sedimentary environment and late tectonic events control the types of unconventional gas accumulation systems: The Paleozoic marine sediments in South China are conducive to the formation of shale gas and tight carbonate gas accumulation systems; The marine–continental transitional facies deposits of Upper Paleozoic distributed in the South and North China are conducive to the development of coalbed methane, shale gas and tight sandstone gas accumulation systems; In the Middle and East China, Mesozoic–Cenozoic deep-water continental deposits are more conducive to the deep tight sandstone gas and shale gas accumulation systems, while shallow water continental basins are more conducive to the formation of coalbed methane, tight sandstone gas and shale gas accumulation systems. Water-soluble gas is widely found in the Cenozoic shallow-buried areas rich in organic matter or high-pressure reservoirs adjacent to gas source rocks.

A MPS-based novel method of reconstructing 3D reservoir models from 2D images using seismic constraints

While multi-point geostatistics (MPS) be used to model complex underground reservoirs, this method relies heavily on high-quality three-dimensional (3D) training images that may be difficult to acquire in real field studies. Furthermore, the original MPS technique does not include seismic data, which may noticeably improve underground reservoir predictions. Our proposed model, Se3DRCS, is a novel method to reconstruct 3D geological models from two-dimensional (2D) images and seismic information. In Se3DRCS, we are able to perform MPS-based modeling using 2D training images, rather than 3D images. These 2D images are produced by analyzing well profiles and sedimentary facies planes. After using the Se3DRCS method to obtain an initial geological model, we then generate elastic parameters and synthetic seismograms via direct sampling and convolution with the extracted seismic wavelets, respectively. By comparing the observed and synthetic seismic records, we refine the simulation results using the adaptive sampling method. The initial geological model is then updated with conditional data after each subsequent simulation iteration. When the error between the observed and synthetic seismograms falls below a reasonable threshold value, we have arrived at the best-fitting geological model. Our results indicate that the Se3DRCS method is capable of predicting the data elicited from wells with an accuracy of up to 82.88%, providing constraints on the distribution of branch channels in a delta reservoir, and reproducing local complex non-stationary geological features.

Facies-Controlled Modeling for Permeability of Tight Gas Reservoir Based on Hydrodynamic and Geophysics Characteristics

Due to poor physical properties and strong heterogeneity of Daniudi tight gasfield (China), traditional methods are not ideal for predicting reservoir permeability. Based on geoscience data mining algorithms and modeling techniques, this parameter is predicted and characterized from a new perspective. The high precision hydraulic unit information was outputted by the BP neural network and the interwell database was predicted by sequential indicator simulation based on the logging and core data, the exponential and power law relationship functions were selected to participate in the construction of the permeability model. The results showed that the facies-controlled modeling system for permeability based on the hydrodynamic characteristics of tight gas reservoirs could effectively improve the accuracy of reservoir prediction, and the logging information in the longitudinal direction and the facies information in the plane were combined by the hydraulic unit.

The Influence of Interference Layer on the Prediction of Seismic Information of Channel Sedimentary Reservoir

Channel sedimentary reservoirs are the main oil and gas accumulation sites at home and abroad. With the deepening of oil and gas exploration, channel-related oil and gas reservoirs with obvious seismic characteristics have been mostly discovered, and the rest are basically oil and gas reservoirs with unobvious seismic characteristics and difficult to be discovered. The difficulty and risk of discovering these oil and gas reservoirs are on the increase. In order to reduce the exploration risk, it is necessary to form a more reliable time window control theory with a better seismic interpretation technique for channel reservoir reliability. In this paper, 2D forward modeling was used to study the influence of the upper and lower interference layers on the imaging of channel reservoir. When the lithological composition is relatively stable, the degree of interference of the interference layer is closely related to the seismic wavelet, and it is especially susceptible to the main frequency of the seismic wavelet. The lower the main frequency of seismic wavelet, the greater the degree of interference of interference layers on the imaging of channel reservoir, and vice versa. Further analysis on the characteristics of the Ricker wavelet was made and showed that the influence of the upper and lower interference layers on the imaging information of the channel reservoir is concentrated in half apparent major period. Then, considering the dynamic change of distance between the interference layers and the top and bottom interfaces of channel reservoir, the influence of interference layers on the imaging of channel reservoir was studied through 1D forward modeling. The interference area was divided into maximum interference area and main interference area depending on the degree of influence. On this basis, the time window control theory of channel reservoir prediction was clarified. The seismic information which reflects the channel reservoir reliably can be obtained by avoiding the maximum interference area, especially the main interference area.