Seismic identification and characterization of complex storage space oil and gas reservoirs

To predict complex reservoir spaces(with developed caves,pores,and fractures),based on the results of full-azimuth depth migration processing,we adopted reverse weighted nonlinear inversion to improve the accuracy of porous reservoir prediction.Scattering imaging three-parameter wavelet transform technology was used to accurately predict small-scale cave bodies.The joint inversion method of velocity and amplitude anisotropy was developed to improve the accuracy of small and medium-sized fracture prediction.The results of multiscale fracture modeling and characterization,interwell connectivity analysis,and connection path prediction are consistent with the production condition.Finally,based on the above prediction findings,favorable reservoir development areas were predicted.The above ideas and strategies have great application value for the efficient exploration and development of complex storage space reservoirs and the optimization of high-yield well locations.

Fault Interior Structure Index Construction and Quantitative Seismic Interpretation of Fault Facies

Fault zones have always drawn attention from the industry,and the performance of a quantitative interpretation of the fault interior structure on seismic data has remained as a big challenge.In this work,we focus on quantitatively populating the heterogeneity of the fault interior structure in a three-dimensional space on seismic data.To realize this goal,we take the South Wuerxun sub-depression in Hailar Basin as an example of a faulted basin.First,based on a heterogeneity analysis using the drilling and logging information from wells,we establish fault zone geologic models and perform seismic forward modeling to determine the relationship between different fault zone models with different fault dips,internal fillings,and P wave responses.Next,the fault interior structure index(FIS)is constructed,and the response features on the FIS from the fault facies and the host rock are observed.Finally,the FIS is applied to perform the quantitative interpretation and prediction of the heterogeneity in the FIS on seismic data.The results show that the FIS response from the fault zone is higher than that of the host rock in plane,indicating that the former is quantitatively separated from the surrounding host rocks.The FIS values greater than 26 represent the feedback from the fault facies,whereas those less than 26 represent the response from the host rocks.The FIS shows segmented features in the strike,banded in dip on plane.On the slip surface,the FIS indicates the shale smear zone and the location where low-graded and small-scaled faults are densely developed.The heterogeneity prediction result is proven by oil and gas exploration activities.The study results imply that the FIS could indicate a favorable path of the hydrocarbon migration in the fault zone and evaluate the fault sealing parts.The method can explore the quantitative characterization of fault facies and has essential popularization and application values in similar geological application fi elds,including hydrocarbon exploration,development of faulted reservoirs,and geological engineering evaluation related to faults.

Well Logging Stratigraphic Correlation Algorithm Based on Semantic Segmentation

Well logging curves serve as indicators of strata attribute changes and are frequently utilized for stratigraphic analysis and comparison.Deep learning,known for its robust feature extraction capabilities,has seen continuous adoption by scholars in the realm of well logging stratigraphic correlation tasks.Nonetheless,current deep learning algorithms often struggle to accurately capture feature changes occurring at layer boundaries within the curves.Moreover,when faced with data imbalance issues,neural networks encounter challenges in accurately modeling the one-hot encoded curve stratifi cation positions,resulting in signifi cant deviations between predicted and actual stratifi cation positions.Addressing these challenges,this study proposes a novel well logging curve stratigraphic comparison algorithm based on uniformly distributed soft labels.In the training phase,a label smoothing loss function is introduced to comprehensively account for the substantial loss stemming from data imbalance and to consider the similarity between diff erent layer data.Concurrently,spatial attention and channel attention mechanisms are incorporated into the shallow and deep encoder stages of U²-Net,respectively,to better focus on changes in stratifi cation positions.During the prediction phase,an optimized confi dence threshold algorithm is proposed to constrain stratifi cation results and solve the problem of reduced prediction accuracy because of occasional layer repetition.The proposed method is applied to real-world well logging data in oil fi elds.Quantitative evaluation results demonstrate that within error ranges of 1,2,and 3 m,the accuracy of well logging curve stratigraphic division reaches 87.27%,92.68%,and 95.08%,respectively,thus validating the eff ectiveness of the algorithm presented in this paper.

N-th root slant stack for enhancing weak seismic signals

Seismic imaging of complicated underground structures with severe surface undulation(i.e.,double complex areas)is challenging owing to the difficulty of collecting the very weak reflected signal.Enhancing the weak signal is difficult even with state-of-the-art multi-domain and multidimensional prestack denoising techniques.This paper presents a time–space dip analysis of offset vector tile(OVT)domain data based on theτ-p transform.The proposed N-th root slant stack method enhances the signal in a three-dimensionalτ-p domain by establishing a zero-offset time-dip seismic attribute trace and calculating the coherence values of a given data sub-volume(i.e.,inline,crossline,time),which are then used to recalculate the data.After sorting,the new data provide a solid foundation for obtaining the optimal N value of the N-th root slant stack,which is used to enhance a weak signal.The proposed method was applied to denoising low signal-to-noise ratio(SNR)data from Western China.The optimal N value was determined for improving the SNR in deep strata,and the weak seismic signal was enhanced.The results showed that the proposed method effectively suppressed noise in low-SNR data.

Classification of tight sandstone reservoirs based on NMR logging

The traditional reservoir classification methods based on conventional well logging are inefficient for determining the properties,such as the porosity,shale volume,J function,and flow zone index,of the tight sandstone reservoirs because of their complex pore structure and large heterogeneity.Specifically,the method that is commonly used to characterize the reservoir pore structure is dependent on the nuclear magnetic resonance(NMR)transverse relaxation time(T2)distribution,which is closely related to the pore size distribution.Further,the pore structure parameters(displacement pressure,maximum pore-throat radius,and median pore-throat radius)can be determined and applied to reservoir classification based on the empirical linear or power function obtained from the NMR T2 distributions and the mercury intrusion capillary pressure ourves.However,the effective generalization of these empirical functions is difficult because they differ according to the region and are limited by the representative samples of different regions.A lognormal distribution is commonly used to describe the pore size and particle size distributions of the rock and quantitatively characterize the reservoir pore structure based on the volume,mean radius,and standard deviation of the small and large pores.In this study,we obtain six parameters(the volume,mean radius,and standard deviation of the small and large pores)that represent the characteristics of pore distribution and rock heterogeneity,calculate the total porosity via NMR logging,and classify the reservoirs via cluster analysis by adopting a bimodal lognormal distribution to fit the NMR T2 spectrum.Finally,based on the data obtained from the core tests and the NMR logs,the proposed method,which is readily applicable,can effectively classify the tight sandstone reservoirs.

Multi-resolution graph-based clustering analysis for lithofacies identifi cation from well log data: Case study of intraplatform bank gas fi elds, Amu Darya Basin

In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.

Optimizing the wavefield storage strategy in reflection-acoustic logging reverse-time migration

In this paper,wavefield storage optimization strategies are discussed with respect to reverse-time migration(RTM)imaging in reflection-acoustic logging,considering the problem of massive wavefield data storage in RTM itself.In doing so,two optimization methods are proposed and implemented to avoid wavefield storage.Firstly,the RTM based on the excitation-amplitude imaging condition uses the excitation time to judge the imaging time,and accordingly,we only need to store a small part of wavefield,such as the wavefield data of dozens of time points,the instances prove that they can even be imaged by only two time points.The traditional RTM usually needs to store the wavefield data of thousands of time points,compared with which the data storage can be reduced by tens or even thousands of times.Secondly,the RTM based on the random boundary uses the idea that the wavefield scatters rather than reflects in a random medium to reconstruct the wavefield source and thereby directly avoid storing the forward wavefield data.Numerical examples show that compared with other migration algorithms and the traditional RTM,both methods can effectively reduce wavefield data storage as well as improve data-processing efficiency while ensuring imaging accuracy,thereby providing the means for high-efficiency and highprecision imaging of fractures and caves by boreholes.

Random noise suppression for seismic data using a non-local Bayes algorithm

For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.

lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic targets.To solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0<p≤1).Using a numerical model,we demonstrated the higher time–frequency resolution of this method and its capability for improving the seismic interpretation for thin layers.Moreover,given the actual underground geology that can be often complex,we further propose a p-norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature fusion.By comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm constraint.Finally,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western China.The results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of faults.We thus demonstrated that the proposed method has huge application potential in seismic interpretation.

Prediction of brittleness based on anisotropic rock physics model for kerogen-rich shale

The construction of a shale rock physics model and the selection of an appropriate brittleness index （B/） are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the self- consistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BL Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young＇s Modulus were sensitive to variations in lithology, while those using Lame＇s Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.

A graphical model for haloanhydrite components and P-wave velocity:A case study of haloanhydrites in Amu Darya Basin

Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.

Method for obtaining high-resolution velocity spectrum based on weighted similarity

Seismic wave velocity is one of the most important processing parameters of seismic data,which also determines the accuracy of imaging.The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity,producing the velocity spectrum by superposing energy or similarity coefficients.In this method,however,the sensitivity of the semblance spectrum to change of velocity is weak,so the resolution is poor.In this paper,to solve the above deficiencies of conventional velocity analysis,a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed.By introducing two weighting functions,the resolution of the similarity spectrum in time and velocity is improved.Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums;at the same time,the method shows good noise-resistibility.

Seismic AVAZ inversion for orthorhombic shale reservoirs in the Longmaxi area, Sichuan

Seismic AVAZ inversion method based on an orthorhombic model can be used to invert anisotropy parameters of the Longmaxi shale gas reservoir in the Sichuan Basin..As traditional seismic inversion workfl ow does not suffi ciently consider the infl uence of fracture orientation,we predict fracture orientation using the method based on the Fourier series to correct pre-stacked azimuth gathers to guarantee the accuracy of input data,and then conduct seismic AVAZ inversion based on the VTI constraints and Bayesian framework to predict anisotropy parameters of the shale gas reservoir in the study area.We further analyze the rock physical relation between anisotropy parameters and fracture compliance and mineral content for quantitative interpretation of seismic inversion results.Research results reveal that the inverted anisotropy parameters are related to P-and S-wave respectively,and thus can be used to distinguish the effect of fracture and fl uids by the joint interpretation.Meanwhile high values of anisotropy parameters correspond to high values of fracture compliance,so the anisotropy parameters can refl ect the development of fractures in reservoir.There is two sets of data from different sources,including the content of brittle mineral quartz obtained from well data and the anisotropy parameters inverted from seismic data,also show the positive correlation.This further indicates high content of brittle mineral makes fractures developing in shale reservoir and enhances seismic anisotropy of the shale reservoir.The inversion results demonstrate the characterization of fractures and brittleness for the Longmaxi shale gas reservoir in the Sichuan Basin.

Effects of micrite microtextures on the elastic and petrophysical properties of carbonate reservoirs

Apparent differences in sedimentation and diagenesis exist between carbonate reservoirs in different areas and affect their petrophysical and elastic properties.To elucidate the relevant mechanism,we study and analyze the characteristics of rock microstructure and elastic properties of carbonates and their variation regularity using 89 carbonate samples from the different areas The results show that the overall variation regularities of the physical and elastic properties of the carbonate rocks are controlled by the microtextures of the microcrystalline calcite,whereas the traditional classification of rock-and pore-structures is no longer applicable.The micrite microtextures can be divided,with respect to their morphological features,into porous micrite,compact micrite,and tight micrite.As the micrites evolves from the first to the last type,crystal boundaries are observed with increasingly close coalescence,the micritic intercrystalline porosity and pore-throat radius gradually decrease;meanwhile,the rigidity of the calcite microcrystalline particle boundary and elastic homogeneity are enhanced.As a result,the seismic elastic characteristics,such as permeability and velocity of samples,show a general trend of decreasing with the increase of porosity.For low-porosity rock samples(φ<5%)dominated by tight micrite,the micritic pores have limited contributions to porosity and permeability and the micrite elastic properties are similar to those of the rock matrix.In such cases,the macroscopic physical and elastic properties are more susceptible to the formation of cracks and dissolution pores,but these features are controlled by the pore structure.The pore aspect ratio can be used as a good indication of pore types.The bulk modulus aspect ratio for dissolution pores is greater than 0.2,whereas that of the intergranular pores ranges from 0.1 to 0.2.The porous and compact micrites are observed to have a bulk modulus aspect ratio less than 0.1,whereas the ratio of the tight micrite approaches 0.2。

Comparison of different inversion methods of D-T_(2)two-dimensional nuclear magnetic resonance logging and applicability analysis

D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.

Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis

Logging facies analysis is a significant aspect of reservoir description.In particular,as a commonly used method for logging facies identification,Multi-Resolution Graph-based Clustering(MRGC)can perform depth analysis on multidimensional logging curves to predict logging facies.However,this method is very time-consuming and highly dependent on the initial parameters in the propagation process,which limits the practical application effect of the method.In this paper,an Adaptive Multi-Resolution Graph-based Clustering(AMRGC)is proposed,which is capable of both improving the efficiency of calculation process and achieving a stable propagation result.More specifically,the proposed method,1)presents a light kernel representative index(LKRI)algorithm which is proved to need less calculation resource than those kernel selection methods in the literature by exclusively considering those"free attractor"points;2)builds a Multi-Layer Perceptron(MLP)network with back propagation algorithm(BP)so as to avoid the uncertain results brought by uncertain parameter initializations which often happened by only using the K nearest neighbors(KNN)method.Compared with those clustering methods often used in image-based sedimentary phase analysis,such as Self Organizing Map(SOM),Dynamic Clustering(DYN)and Ascendant Hierarchical Clustering(AHC),etc.,the AMRGC performs much better without the prior knowledge of data structure.Eventually,the experimental results illustrate that the proposed method also outperformed the original MRGC method on the task of clustering and propagation prediction,with a higher efficiency and stability.

A DTW distance-based seismic waveform clustering method for layers of varying thickness

Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is applicable for layers of stable thickness.When a layer exhibits variable thickness in the seismic response,a fixed time window cannot provide comprehensive geologic information for the target interval.Therefore,we propose a novel approach for a waveform clustering workfl ow based on a variable time window to enable broader applications.The dynamic time warping(DTW)distance is fi rst introduced to effectively measure the similarities between seismic waveforms with various lengths.We develop a DTW distance-based clustering algorithm to extract centroids,and we then determine the class of all seismic traces according to the DTW distances from centroids.To greatly reduce the computational complexity in seismic data application,we propose a superpixel-based seismic data thinning approach.We further propose an integrated workfl ow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms.We evaluated the performance by applying the proposed workfl ow to synthetic seismograms and seismic survey data.Compared with the the traditional waveform clustering method,the synthetic seismogram results demonstrate the enhanced capability of the proposed workfl ow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness.Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workfl ow correlates well with the geological characteristics of wells in terms of reservoir thickness.

Serial structure multi-task learning method for predicting reservoir parameters

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.

Optimization method of fi rst-arrival waveform inversion based on the L-BFGS algorithm

The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In the full waveform inversion method,adding a cross-correlation function to the objective function can eff ectively reduce the nonlinearity of the inversion process.In this paper,the nonlinearity of this process is reduced by introducing the correlation objective function into the FAWI and by deriving the corresponding gradient formula.We then combine the first-arrival wave travel-time tomography with the FAWI to form a set of inversion processes.This paper uses the limited memory Broyden-Fletcher-Goldfarb-Shanno(L-BFGS)algorithm to improve the computational effi ciency of inversion and solve the problem of the low effi ciency of the FAWI method.The overthrust model and fi eld data test show that the method used in this paper can eff ectively reduce the nonlinearity of inversion and improve the inversion calculation effi ciency at the same time.

Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning

Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.