Reservoir prediction using multi-wave seismic attributes

The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the ＂curse of dimension＂. Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis （PCA）, independent component analysis （ICA） for attribute optimization and support vector machine （SVM） for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Monitoring the change in horizontal stress with multi-wave time-lapse seismic response based on nonlinear elasticity theory

Monitoring the change in horizontal stress from the geophysical data is a tough challenge, and it has a crucial impact on broad practical scenarios which involve reservoir exploration and development, carbon dioxide (CO_(2)) injection and storage, shallow surface prospecting and deep-earth structure description. The change in in-situ stress induced by hydrocarbon production and localized tectonic movements causes the changes in rock mechanic properties (e.g. wave velocities, density and anisotropy) and further causes the changes in seismic amplitudes, phases and travel times. In this study, the nonlinear elasticity theory that regards the rock skeleton (solid phase) and pore fluid as an effective whole is used to characterize the effect of horizontal principal stress on rock overall elastic properties and the stress-dependent anisotropy parameters are therefore formulated. Then the approximate P-wave, SV-wave and SH-wave angle-dependent reflection coefficient equations for the horizontal-stress-induced anisotropic media are proposed. It is shown that, on the different reflectors, the stress-induced relative changes in reflectivities (i.e., relative difference) of elastic parameters (i.e., P- and S-wave velocities and density) are much less than the changes in contrasts of anisotropy parameters. Therefore, the effects of stress change on the reflectivities of three elastic parameters are reasonably neglected to further propose an AVO inversion approach incorporating P-, SH- and SV-wave information to estimate the change in horizontal principal stress from the corresponding time-lapse seismic data. Compared with the existing methods, our method eliminates the need for man-made rock-physical or fitting parameters, providing more stable predictive power. 1D test illustrates that the estimated result from time-lapse P-wave reflection data shows the most reasonable agreement with the real model, while the estimated result from SH-wave reflection data shows the largest bias. 2D test illustrates the feasibility of the proposed inversion method for estimating the change in horizontal stress from P-wave time-lapse seismic data.

Seismic attribute extraction based on HHT and its application in a marine carbonate area

The Hilbert-Huang transform（HHT） is a new analysis method suitable for nonlinear and non-stationary signals.It is very appropriate to seismic signals because they show both non-stationary and nonlinear characteristics.We first introduce the realization of HHT empirical mode decomposition（EMD） and then comparatively analyze three instantaneous frequency algorithms based on intrinsic mode functions（IMF） resulting from EMD,of which one uses the average instantaneous frequency of two sample intervals having higher resolution which can determine that the signal frequency components change with time.The method is used with 3-D poststack migrated seismic data of marine carbonate strata in southern China to effectively extract the three instantaneous attributes.The instantaneous phase attributes of the second intrinsic mode functions（IMF2） better describe the reef facies of the platform margin and the IMF2 instantaneous frequency attribute has better zoning.Combining analysis of the three IMF2 instantaneous seismic attributes and drilling data can identify the distribution of sedimentary facies well.

Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application

The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain.Using seismic forward modeling of a typical thin channel sand body,a new seismic attribute-the ratio of peak frequency to amplitude was constructed.Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies,while the amplitude attribute is sensitive to the strata lithology.The ratio of the two attributes can highlight the boundaries of the channel sand body.Moreover,the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness.Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well.The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries.Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.

New seismic attribute:Fractal scaling exponent based on gray detrended fluctuation analysis

Seismic attributes have been widely used in oil and gas exploration and development. However, owing to the complexity of seismic wave propagation in subsurface media, the limitations of the seismic data acquisition system, and noise interference, seismic attributes for seismic data interpretation have uncertainties. Especially, the antinoise ability of seismic attributes directly affects the reliability of seismic interpretations. Gray system theory is used in time series to minimize data randomness and increase data regularity. Detrended fluctuation analysis （DFA） can effectively reduce extrinsic data tendencies. In this study, by combining gray system theory and DFA, we propose a new method called gray detrended fluctuation analysis （GDFA） for calculating the fractal scaling exponent. We consider nonlinear time series generated by the Weierstrass function and add random noise to actual seismic data. Moreover, we discuss the antinoise ability of the fractal scaling exponent based on GDFA. The results suggest that the fractal scaling exponent calculated using the proposed method has good antinoise ability. We apply the proposed method to 3D poststack migration seismic data from southern China and compare fractal scaling exponents calculated using DFA and GDFA. The results suggest that the use of the GDFA-calculated fractal scaling exponent as a seismic attribute can match the known distribution of sedimentary facies.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

Ordovician limestone aquosity prediction using nonlinear seismic attributes:Case from the Xutuan coal mine

Ordovician limestone water is coal mines. In this paper, we analyze the the main source of water inrush in North China characteristic of three kinds of nonlinear seismic attributes, such as the largest lyapunov exponent,fractal dimension and entropy and introduce their calculation methods. Taking the 81st and 82nd coal districts in the Xutuan coal mine as examples, we extract the three seismic attributes based on the 3D prestack migration seismic data of this area, which can display the Ordovician limestone fracture distribution in the mine. We comprehensively analyzed the three nonlinear seismic attributes and compared the results with transient electromagnetic exploration results and determined the possible Ordovician limestone aquosity distribution. This demonstrated that the nonlinear seismic attributes technology is an effective approach to predict the aquosity of Ordovician limestone.

Numerical Simulation of Bubble Plumes and an Analysis of Their Seismic Attributes

To study the bubble plume's seismic response characteristics,the model of a plume water body has been built in this article using the bubble-contained medium acoustic velocity model and the stochastic medium theory based on an analysis of both the acoustic characteristics of a bubble-contained water body and the actual features of a plume.The finite difference method is used for forward modelling,and the single-shot seismic record exhibits the characteristics of a scattered wave field generated by a plume.A meaningful conclusion is obtained by extracting seismic attributes from the pre-stack shot gather record of a plume.The values of the amplitude-related seismic attributes increase greatly as the bubble content goes up,and changes in bubble radius will not cause seismic attributes to change,which is primarily observed because the bubble content has a strong impact on the plume's acoustic velocity,while the bubble radius has a weak impact on the acoustic velocity.The above conclusion provides a theoretical reference for identifying hydrate plumes using seismic methods and contributes to further study on hydrate decomposition and migration,as well as on distribution of the methane bubble in seawater.

Seismic attributes and integrated prediction of fractured and caved carbonate reservoirs in the Tarim Basin,China

The carbonate reservoirs in the Tarim Basin are characterized by anisotropy and strong heterogeneity.Combined with an integrated analysis of data from seismic,geology,and drilling results,a series of attributes which are suitable for fractured and caved carbonate reservoir prediction is discussed,including amplitude,coherence analysis,spectra decomposition,seismic absorption attenuation analysis and impedance inversion.Moreover,3-D optimization of these attributes is achieved by integration of multivariate discriminant analysis and principle component analysis,where the logging data are taken as training samples.Using the optimized results,the spatial distribution and configuration features of the caved reservoirs can be characterized in detail.This technique not only improves the understanding of the spatial distribution of current reservoirs but also provides a significant basis for the discovery and production of carbonate reservoirs in the Tarim Basin.

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.

Correction of seismic attribute-based smallstructure prediction errors using GPR data-a case study of the Shuguang Coal Mine,Shanxi

Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.

Forward modeling and inversion of the relation model between the gas content of plume and its seismic attribute

The methane bubble plume attracts interest because it offers direct evidence of seafloor gas leakage and plays an indirect role in the exploration and identification of natural gas hydrate.In this study,based on established plume models and their migration sections,three amplitude-class attributes were extracted from three formations for the migration sections of five plumes,and the correlation between the gas content and seismic attribute was obtained.As the gas content increases,the amplitude attribute correspondingly increases,and the linear correlation is relatively good.Moreover,correlation coefficients between gas content and amplitude attributes are close to 1.0.By using linear fitting,the relation model between the gas content of the plume and the seismic attribute was obtained.The relation model was subsequently used to invert the gas content from a real databearing plume.Comparison of the gas content section of the plume with the attribute section and real seismic section reveals common distribution characteristics,namely,the color of the section in the lower right corner is dark.If the amplitude value is large in the seismic section of the real plume,the amplitude attribute value is also large in the corresponding attribute section,and the inverted value of the gas content is also large(because gas content and amplitude are linearly correlated),which indicates that the plume bubbles of the section in the lower right corner is intensively distributed.Finally,the obtained gas content section of the plume can reflect the distribution of the plume bubble content more simply and intuitively,from which the distribution law of seafloor bubbles can be deduced,and this lays a foundation for the further estimation of the gas content of the plume and hydrate reserves.

Application of Seismic Attributes for Delineation of Channel Geometries and Analysis of Various Aspects in Terms of Lithological and Structural Perspectives of Lower Goru Formation, Pakistan

Miano area is one of the distinct major hydrocarbon producing fields of the Lower Indus basin. Lower Goru is the reservoir strait in this area. The aim of the present study is to exploit the channels reservoirs and other stratigraphic features in such a terrain where there is always a challenge for the geoscientist to search and exploit the channeled reservoirs. To resolve this issue we have utilized attribute analysis on high resolution 3D-seismic data for the detailed comparative studies for the channels. There are many astonishing features that are identified in the current study, which could not otherwise be easily enhanced with the help of 2D Seismic Data. Seismic attributes such as coherency, frequency (are also appropriate for lithological discrimination), which are sensitive to the channel edges are applied for the channel delineation and their geometrical analysis. Spectral decomposition techniques are also applied for the delineation of channels and to appropriately select the best band for channels identification. Three types of channel geometries are recognized: 1) highly sinuous channel;2) narrow-broad meandering belts;3) moderate to high sinuous channel. NW-SE, N-W trending faults can be helpful to compartmentalize the reservoir. Instantaneous and dominant frequency are more beneficial for further field development based on Gamma Ray logs from nearby drilled wells and dimensional perspectives analysis of the channel reservoir.

The Integrated Approach to Seismic Attributes of Lithological Characterization of Reservoirs: Case of the F3 Block, North Sea-Dutch Sector

Technological development, particularly the development of signal processing, has contributed to the improvement of the quality of results in the analysis of the characteristics of hydrocarbon reservoirs. This scientific advance is especially marked by the introduction of seismic attributes in the characterization of reservoirs. This paper is aimed at evaluating the contribution of seismic attributes in the lithological characterization of hydrocarbon accumulation zones. Thus, it has been found that instantaneous attributes make it possible to highlight the different properties of reservoir rocks. Techniques for extracting three-dimensional volume attributes have been applied to a 3D seismic data volume of the F3 block in the Dutch sector of the North Sea. The purpose of using these volume attributes is to extract signal characteristics from lithofacies in hydrocarbon zones. The results provide a remarkable contribution to instantaneous seismic attributes in reservoir location, hydrocarbon detection, and reservoir lithological prediction. The instantaneous amplitude, the derivative of amplitude 1, the factor Q, the energy and the Hilbert transform are attributes which highlight the presence of hydrocarbons through the bright spots. Thus, curvature, similarity, dip, and coherence are useful in the determination of traps such as faults and anticlines. The crossing of the instantaneous amplitude and the instantaneous frequency makes it possible to analyze the lithofacies. In the same way, the analysis of the crossed diagrams between the porosity and the instantaneous amplitude made it possible to define the different layers of the reservoir.

Study of fault configuration related mysteries through multi seismic attribute analysis technique in Zamzama gas field area,southern Indus Basin,Pakistan

Seismic attribute analysis approach has been applied for the interpretation and identification of fault geometry of Zamzama Gas Field. Zamzama gas field area, which lies in the vicinity of Kirthar fold and thrust belt, Southern Indus Basin of Pakistan. The Zamzama fault and its related structure have been predicted by applying the Average Energy Attribute, Instantaneous Frequency Attribute, relative Acoustic Impedance Attribute and Chaotic Reflection Attribute on the seismic line GHPK98 A.34. The results have been confirmed by applying the spectral decomposition attribute on the same seismic line that reveal the geometric configuration of Zamzama structure. The fault is reverse and started from 0 s and ended at the depth of 2.5 s on the vertical seismic section. Hanging wall moves up along the fault plane under the action of eastward oriented stress, which formed a large northesouth oriented and eastward verging thrusted anticline.

Application of AVO and Seismic Attributes Techniques for Characterizing Pliocene Sand Reservoirs in Darfeel Field, Eastern Mediterranean, Egypt

Mediterranean Sea considered as a main hydrocarbon province in Egypt as a huge reservoirs have been discovered till now. Port Fouad marine is a gas and condensate field located in Eastern Mediterranean Sea about 30 KM off Egyptian coast, in a water depth of about 30 m. The Concession is operated by PETROBEL on behalf of Petrosaid (Port Said Petroleum Company). The field was put on production on April 1996, from the Miocene turbidities sands of Wakar Formation plus Pilocene Kafr EL Sheikh Formation. Darfeel field is located within Port Fouad Concession, seven wells have been drilled till now and producing from Pliocene section (Kafr El Sheikh Formation). Pliocene is the main reservoir in Darfeel field which characterized by turbidities sand stone. The aim of this work is to identify the distribution of turbidities sand and characterize sand reservoirs using AVO (amplitude verses offset) and seismic attributes techniques. The workflow is starting from conventional seismic interpretation, maps (time, depth, and amplitude), depositional environments, and finally structure setting. In addition to use some of unconventional seismic interpretation such as seismic attributes. AVO analysis and attributes had been applied in a temp of differentiate between gas sand reservoirs and non-gas reservoirs. The final result aid to identify the reservoir distribution and characterization of sand reservoirs through the field. So, the use of different seismic techniques is powerful techniques in identifying reservoir distribution.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs,necessitating that diverse kinds of information are used to infer these properties.In this study,the seismic data related to the Hendijan oil field were utilised,along with the available logs of 7 wells of this field,in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals.After the overall survey of data,a seismic line was selected and seismic inversion methods(model-based,band limited and sparse spike inversion)were applied to it.Amongst all of these techniques,the model-based method presented the better results.By using seismic attributes and artificial neural networks,the shale volume was then estimated using three types of neural networks,namely the probabilistic neural network(PNN),multi-layer feed-forward network(MLFN)and radial basic function network(RBFN).