Reservoir Prediction Accuracy Research Articles

Structural and seismic attribute analysis of the Paleocene carbonate reservoir from the Balkassar Field, Potwar Plateau, Pakistan

The structural and seismic properties of the Paleocene carbonate reservoir are re-evaluated, using 3D seismic data multi-attributes, to improve reservoir prediction accuracy rarely documented earlier. This study highlights a number of relevant post-stack seismic attributes for understanding the structural setup and seismic properties of a carbonate reservoir. The structural analysis was carried out using seismic interpretation, combined with multi-attribute analysis for prospect actualization and generation of a 3D structural model to determine the structural aspect. The structural interpretation reveals the presence of faults and an asymmetrical anticlinal structure demonstrating a four-way closure, confirming compressional tectonics. The high amplitude anomaly (-6 to 12) combined with the RMS attribute (23 to 32) suggests bright spots in the Paleocene carbonate reservoir and gas saturated zone, the variance attribute (0.05 to 0.03) highlights the presence of two major faults, and the lower instantaneous frequency demonstrates the presence of hydrocarbons. The attribute results indicate that the Lockhart Limestone possesses good reservoir potential as revealed by DHI (bright spot), gas saturated zone and a trapping mechanism supported by four-way structure closure in the region and can help to understand analogous carbonate reservoirs in similar geological settings around the world.

Using seismic petrophysical modeling and prestack simultaneous inversion to provide insights into the physical properties of uranium-bearing reservoirs: Implications for favorable sites of sandstone-hosted uranium deposits

Seismic prospecting has been accepted as one of the most widely available methods for exploring sandstone-hosted uranium deposits (SUDs). However, conventional seismic interpretation faces a challenge in the identification and characterization of a uranium reservoir’s complexity. How to characterize in detail a uranium reservoir’s physical complexity and effectively improve uranium reservoir prediction accuracy remain unresolved problems. To address this, we develop a novel combination of petrophysical modeling and prestack simultaneous inversion to understand in detail the physical properties of uranium-bearing reservoirs and efficiently predict favorable SUD sites. First, we develop a workflow of rock-physics modeling for SUD logs using the Xu-White method to calculate the modulus of elasticity of the grain matrix; subsequently, we extend the Walton model for the modulus prediction of the dry rocks and the Gassmann equation for one of the saturated rocks after a massive calculation test; and then, we predict the S-wave data used for the following inversion. Second, we execute a prestack simultaneous inversion to obtain the petrophysical parameters (e.g., P-impedance, density [[Formula: see text]], shear modulus [[Formula: see text]], Lamé coefficient [[Formula: see text]], and Young’s modulus) that can provide insights into the physical properties of a uranium metallogenic environment. Accordingly, we discover that sites bearing uranium mineralization strongly correspond to areas with low elastic-parameter values (especially [Formula: see text] and [Formula: see text]), whereas nonuranium anomalies occur in high-value sites. This indicates that weakened elastic characteristics are caused by the enhancement of the total organic content and total clay mineral volumes of the uranium-bearing layers. In summary, the developed combination approach can yield an effective and accurate characterization of the geologic properties of uranium-bearing formations, and it can provide prediction factors (e.g., parameters related to the shear modulus) for uranium mineralization.

Quantitative identification and prediction of mixed lithology, Bohai Sea, China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Research and application of 5D seismic prediction technology

With the development of wide orientation, wide band, and high-density acquisition and processing technology, the original 3D (time, line number, and track number) seismic data have expanded to two dimensions — the addition of offset distance information and azimuth information, resulting in 5D seismic data. In this paper, we explore three aspects. Identifying microfaults through subazimuth superposition and poststack coherent body techniques; predicting fractures by means of prestack anisotropy, poststack dip, azimuth, coherence, and curvature; and inscribing river channels with pure longitudinal wave near-zero incidence angle reflection information — that is, the exploration of 5D seismic prediction techniques. Field applications in different types of geological bodies in the Lujiabao Depression and Ordos Basin proved that the 5D seismic prediction technology greatly improved the accuracy of reservoir prediction and the degree of implementation of microfaults, effectively guiding the deployment of wells and improving the drilling success rates.

CBM reservoir thickness prediction using the seismic nonlinear stochastic inversion method

Due to the complex reservoir conditions and rapid changes in lithological facies in seismic exploration, predicting coalbed methane (CBM) reservoirs is quite challenging. Conventional inversion methods are not highly effective at predicting reservoir thickness, and cannot keep up with current demands. Our aim is to demonstrate how seismic data can be used to forecast coal thickness, as well as the distribution and orientation of subtle structures that may be linked to enhanced permeability zones. In this study, we used a nonlinear stochastic inversion method based on making full use of seismic data and constraining it with known information, such as drilling and logging analysis. This method has been successfully applied in a mining area in Wuxiang County in the southeastern part of Shanxi Province. Compared with the actual geological data, it is found that the prediction accuracy is consistent with the drilling results, and the distribution of the predicted CBM reservoir thickness is consistent with the geological information. Furthermore, due to the randomness of the subsurface medium, the accuracy of reservoir prediction can be increased by observing the target layer seismic reflection wave amplitude, frequency, and other properties.

P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network

Compressional wave velocity (P-wave) plays an important role in seismic amplitude versus offset (AVO) forward and inversion modeling and reservoir prediction. However, due to the high cost and difficulty of P-wave logging, the actual acquired P-wave data is limited, which generally does not suffice for practical applications. To provide reliable data support for oil and gas reservoir exploration, a P-wave prediction method under multi-source spatiotemporal feature fusion and physics-informed neural network (MSTFPIN) is proposed. First, a spatiotemporal hybrid model (STHN) is constructed by combining convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) neural network to accurately express the spatial and temporal characteristics of the logging curves, which utilizes the local perception ability of CNN and long short-term memory function of BLSTM to mine their local shape and depth trends. Then, seismic data are introduced as an additional input of multi-source information, which fills the gap of inter-well geological information, and feature fusion is used to fully exploit the information from multiple sources to enhance the available data. Finally, a physics-informed neural network (PINN) is constructed based on the idea of seismic forward modeling in petroleum engineering to guide the training process of the prediction model, which ensures that the prediction results are consistent with geological laws. P-wave prediction experiments are conducted using real logging and seismic data to verify the effectiveness and generalization ability of the proposed method compared with other methods. The experimental results show that the proposed MSTFPIN model exhibit better prediction performance, and the correlation coefficient and coefficient of determination reach 0.9619 and 0.9234, respectively. Compared with traditional empirical and regression models, the proposed MSTFPIN exhibited higher accuracy and generalization. Overall, our results demonstrate the proposed method can be implemented in real oil fields to improve the accuracy of reservoir prediction.

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.

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.

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.

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.

Lithology and fluid discrimination using combined seismic attributes with the constraint of rock physics: a case study from W field, South Sumatra Basin

Reservoir characterization and fluid discrimination based on seismic reflection amplitude play important roles in seismic exploration industry. Stable fluid-sensitive attributes from seismic data can help reduce uncertainties of hydrocarbon prediction in interwell locations and increase the reliability of drilling plans. In this study, in combination with seismic AVO inversion impedance and the rock physics template analysis, we proposed a new attribute (DI) to discriminate the hydrocarbon-associated anomalies and predict the reservoir parameters including porosity and water saturation quantitatively in Upper Gumai formation of W field, South Sumatra Basin. The new attribute (DI) is constructed using a set of combined impedances derived from prestack seismic inversion with the constraint of rock physics. Numerical modelling based on patchy-saturated model was implemented to test the sensitivity and stability of the new attribute and results showed that the DI attribute can predict the existence of hydrocarbon-filled sands with less ambiguity. With known well log data in W field, a feasibility study including cross-plotting and histogram methods was carried out and concluded that the DI attribute contributes higher resolution in distinguishing the hydrocarbon-filled sands from the background trend. In the process of quantitative interpretation, the DI attribute shows a good regression relationship with porosity and water saturation properties within the hydrocarbon sands with the correlation coefficients reaching 92% and 85%, respectively. By using seismic AVO inversion impedance, combining the application of the DI attribute and Vp/Vs ratio for lithology and fluid contents discrimination was conducted to improve the reservoir prediction accuracy in the field. Application results show that low values of the DI attribute (<18,000 m/s.g/cc) was indicative of hydrocarbon-filled sands effectively while Vp/Vs ratio presented a higher resolution in separating wet sand, dry-sand from shale. By using the linear regression relationship derived from cross-plotting analysis with well data, we calculated the volumes of porosity and water saturation from DI attribute and successfully screened out the hydrocarbon accumulation distribution, which reveals the potential zones of exploration interest in South Sumatra Basin.

Prediction Technique of Thin Sandstone Reservoir Affected by Dual Coal Seam

The reservoir thickness of sub-member of Lower Permain in the south of the eastern margin of Ordos Basin is thin and large variable i laterally. And the seismic response characteristics of sandstone reservoir are doubly interfered by the strong reflection sidelobes of overlying 5# coal seam and underlying 8# coal seam. In view of this situation, in order to better eliminate the influence of upper and lower coal seams and improve the accuracy of reservoir prediction, the strategy of de-strong seismic reflection and de-logging (curve) interference is adopted to carry out the matching of logging and seismic, and on this basis, uses the seismic waveform indication inversion to predict the thin sandstone. The steps are as follows: firstly, by using wavelet decomposition and reconstruction technology, select the seismic wavelet component that can better characterize the reservoir or oil & gas to reasonablely reconstruct seismic data, weaken the influence of the strong reflection from overlying 5# coal seam and underlying 8# coal seam, and highlight the response of weak seismic reflection of tight sandstone in Shan 23 sub-member; Then, the acoustic value of coal seam is corrected to that of mudstone on the logging curve to maintain the consistency of logseismic data. This method realizes the effective identification of sandstone reservoir in Shan 23 sub-member, and greatly improves the target-penetrated rate and sandstone-penetrated rate of horizontal wells.

A method of seismic meme inversion and its application

Abstract Under the condition of thin interbeds with great lateral changes in terrestrial basins, a seismic meme inversion method is established based on the analysis of seismic sedimentology technology. The relationship between seismic waveform and high-frequency well logs is established through dynamic clustering of seismic waveform to improve the vertical and horizontal resolution of inversion results; meanwhile, by constructing the Bayesian inversion framework of different seismic facies, the real facies controlled inversion is realized. The forward model verification results show that the seismic meme inversion can realize precise prediction of 3 m thick thin interbeds, proving the rationality and high precision of the method. The application in the Daqing placanticline shows that the seismic meme inversion could identify 2 m thin interbeds, and the coincidence rates of inversion results and drilling data were more than 80%. The seismic meme inversion method can improve the accuracy of reservoir prediction and provides a useful mean for thin interbeds prediction in terrestrial basins.

Research on Reservoir Prediction With High Precision Based on the Control of Logging and Seism

With the continuous development of detailed reservoir description technology, the research of multiple disciplines reservoir based on the geological cognition has gradually carried out in Daqing oilfields. In this paper, the research mainly studied Putaohua oil layer of Xingshi district, and the study about the method of improving the reservoir prediction accuracy was carried out after finishing the basic data, geological statistics formation, fine seismic horizon interpretation and fault interpretation which based on the combination of logging and seism. Optimize the properties for predicting the structure through seismic attribute and post-stack density pseudo wave impedance inversion to realize quantitative characterization of sandstone and mudstone threshold value by seismic attributes, and coincidence rate showed that, the prediction accuracy of interwell channel sand body reached 85%, and results of block application showed that narrow channel sand had better continuity after the combination of logging and seism, boundary and swing position of river changed large, and interwell connectivity is more accurate, which provides a more reliable basis for the further development of the residual oil in channel sand bodies.

Application of high-resolution sequence stratigraphy in improving the precision of reservoir prediction

Abstract On the basis of high-resolution sequence stratigraphy, this paper establishes a sequence stratigraphic frame, and predicts reservoirs by analyzing velocity field, seismic inversion, and seismic attributes. In the case study of the Jurassic Toutunhe Formation braided river delta reservoir of Shinan 21 well block, middle of the Junggar Basin, space variant velocity field inversion and reservoir prediction were carried out based on the sequence stratigraphic frame and the reservoir prediction accuracy was improved. Comparing the prediction results with the real drilling shows that, when the burial depth of bed of interest is 2 500 m, the maximum prediction error of depth is only 10 m, and the average relative prediction error is 0.16%; for appraisal wells with an average effective sandstone of 13 m, the maximum prediction error of sandstone thickness is only 5 m. The structure mapping and reservoir prediction constrained by the sequence stratigraphic frame achieved success.

Application of multi-component seismic exploration in the exploration and production of lithologic gas reservoirs

The multi-component seismic exploration technique has drawn great attention in the petroleum industry because it enlarges the S-wave information, reduces the ambiguity in reservoir prediction, and improves the prediction and identification possibility of reservoir fluids. To push forward the application and development of the technique, the 2D and 3D multi-component technical tests have been carried out since 2002 in the Sulige Gasfield, Ordos Basin; the Guang'an Gasfield, Sichuan Basin; the Xushen Gasfield, Songliao Basin; and the Sanhu area, Basin, Qaidam Basin. The imaging of the converted wave is better than that of the P-wave for the gas-bearing structures in the deep igneous body of the Songliao Basin and the Sanhu area, and can define the reservoir boundaries more accurately. In the description of the tight sand gas reservoirs of the Sulige and Guang'an Gasfields, the converted wave imaging improves the accuracy of reservoir prediction and fluid identification, paving the way for the arrangement of wells.

Seismic attributes optimization and application in reservoir prediction

Petroleum geophysicists recognize that many parameters related to oil and gas reservoirs are predicted using seismic attribute data. However, how best to optimize the seismic attributes, predict the character of thin sandstone reservoirs, and enhance the reservoir description accuracy is an important goal for geologists and geophysicists. Based on the theory of main component analysis, we present a new optimization method, called constrained main component analysis. Modeling estimates and real application in an oilfield show that it can enhance reservoir prediction accuracy and has better applicability.

05/00056 Proposal of a new rhelogical model of a highly loaded coal-water mixture (CWM): Matsuo, S. Kagaku Kogaku Ronbunshu, 2003, 29, (4), 526–533. (In Japanese)

The multi-component seismic exploration technique has drawn great attention in the petroleum industry because it enlarges the S-wave information, reduces the ambiguity in reservoir prediction, and improves the prediction and identification possibility of reservoir fluids. To push forward the application and development of the technique, the 2D and 3D multi-component technical tests have been carried out since 2002 in the Sulige Gasfield, Ordos Basin; the Guang'an Gasfield, Sichuan Basin; the Xushen Gasfield, Songliao Basin; and the Sanhu area, Basin, Qaidam Basin. The imaging of the converted wave is better than that of the P-wave for the gas-bearing structures in the deep igneous body of the Songliao Basin and the Sanhu area, and can define the reservoir boundaries more accurately. In the description of the tight sand gas reservoirs of the Sulige and Guang'an Gasfields, the converted wave imaging improves the accuracy of reservoir prediction and fluid identification, paving the way for the arrangement of wells.

05/00057 Synthesis and characterization of vegetable oil derived esters: evaluation for their diesel additive properties: Dmytryshyn, S. L. et al. Bioresource Technology, 2004, 92, (1), 55–64

The multi-component seismic exploration technique has drawn great attention in the petroleum industry because it enlarges the S-wave information, reduces the ambiguity in reservoir prediction, and improves the prediction and identification possibility of reservoir fluids. To push forward the application and development of the technique, the 2D and 3D multi-component technical tests have been carried out since 2002 in the Sulige Gasfield, Ordos Basin; the Guang'an Gasfield, Sichuan Basin; the Xushen Gasfield, Songliao Basin; and the Sanhu area, Basin, Qaidam Basin. The imaging of the converted wave is better than that of the P-wave for the gas-bearing structures in the deep igneous body of the Songliao Basin and the Sanhu area, and can define the reservoir boundaries more accurately. In the description of the tight sand gas reservoirs of the Sulige and Guang'an Gasfields, the converted wave imaging improves the accuracy of reservoir prediction and fluid identification, paving the way for the arrangement of wells.

Forward Modeling and Inversion Methods Based on Generalized Elastic Impedance in Seismic Exploration

AbstractConventional seismic trace inversion method is based on the assumption of Pwave striking interface normally. However, conventional multi‐channel seismic data records are oblique reflections, reflection amplitude is the average value of CDP gathers. Using conventional seismic trace inversion method, we can not obtain reliable acoustic impedance (AI) or reservoir properties. Following the idea of Patrick Connolly's (1999) Elastic Impedance (EI), this paper further approximated the Zoeppritz equation. derived the P‐wave reflectivity recursive formula suitable the post‐stack and seismic data of oblique reflection, and proposed Generalized Elastic Impedance (GEI). The key problem of forward modeling and inversion of seismic trace is solved better than ever. GEI not only includes AI, but also includes VP, VS and other lithological parameters. It will have high value when applied in practice. By means of GEI inversion, we can obtain more and more reliable information of fluids, porosity, sand to mud ratio and other lithological. It is helpful to interpret the pitfall in conventional seismic trace inversion and trace integration. It is also helpful to decrease non‐uniqueness in conventional seismic trace inversion and improve the accuracy of reservoir prediction.