查看更多>>摘要：Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deter-ministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi Ⅱ model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the pro-posed method are verified by the field seismic data.
查看更多>>摘要：Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.
查看更多>>摘要：Simultaneous source technology,which reduces seismic survey time and improves the quality of seismic data by firing more than one source with a narrow time interval,is compromised by the massive blended interference.Therefore,deblending algorithms have been developed to separate this interference.Recently,deep learning(DL)has been proved its great potential in suppressing the interference.The most popular DL method employs neural network as a filter to attenuate the blended noise in an iterative estimation and subtraction framework(IESF).However,there are still amplitude distortion and blended noise residual problems,especially when dealing with weak signal submerged in strong interference.To address these problems,we propose a hybrid WUDT-NAFnet,which contains two sub-networks.The first network is a wavelet based U-shape deblending transformer network(WUDTnet),incorporated into IESF as a robust regularization term to iteratively separate the blended interference.The second network is a nonlinear activate free network(NAFnet)designed to recover the event amplitude and further suppress the weak noise residual in IESF.With the hybrid network,the blended noise can be separated pur-posefully and accurately.Examples using synthetic and field seismic data demonstrate that the WUDT-NAFnet outperforms traditional curvelet transform(CT)based method and the deblending transformer(DT)model in terms of deblending.Additionally,for field applications,the data augmentation method of bicubic interpolation is applied to mitigate the feature difference between synthetic and field data.Consequently,the trained network exhibits strong signal preservation ability in numerical field example without requiring additional training.
查看更多>>摘要：The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber com-ponents in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial low-frequency inversion to ensure the stability of the inversion,and use more high-wavenumber compo-nents in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.
查看更多>>摘要：(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.
查看更多>>摘要：The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decision-making/optimization of field development planning.The approach for parameterizing the facies distri-bution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic in-formation,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distri-bution.The history-matching of the facies models shows a good estimation and uncertainty quantifi-cation of facies distribution,a good data match and prediction capabilities.
查看更多>>摘要：Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.
Shohreh IrajiTales Rodrigues De AlmeidaEddy Ruidiaz MunozMateus Basso...
1719-1738页
查看更多>>摘要：This study investigates the impact of pore network characteristics on fluid flow through complex and heterogeneous porous media,providing insights into the factors affecting fluid propagation in such systems.Specifically,high-resolution or micro X-ray computed tomography(CT)imaging techniques were utilized to examine outcrop stromatolite samples of the Lagoa Salgada,considered flow analogous to the Brazilian Pre-salt carbonate reservoirs.The petrophysical results comprised two distinct stro-matolite depositional facies,the columnar and the fine-grained facies.By generating pore network model(PNM),the study quantified the relationship between key features of the porous system,including pore and throat radius,throat length,coordination number,shape factor,and pore volume.The study found that the less dense pore network of the columnar sample is typically characterized by larger pores and wider and longer throats but with a weaker connection of throats to pores.Both facies exhibited less variability in the radius of the pores and throats in comparison to throat length.Additionally,a series of core flooding experiments coupled with medical CT scanning was designed and conducted in the plug samples to assess flow propagation and saturation fields.The study revealed that the heterogeneity and presence of disconnected or dead-end pores significantly impacted the flow patterns and saturation.Two-phase flow patterns and oil saturation distribution reveal a preferential and heterogeneous displacement that mainly swept displaced fluid in some regions of plugs and bypassed it in others.The relation between saturation profiles,porosity profiles,and the number of fluid flow patterns for the samples was evident.Only for the columnar plug sample was the enhancement in recovery factor after shifting to lower salinity water injection(SB)observed.
查看更多>>摘要：In-situ upgrading by heating is feasible for low-maturity shale oil,where the pore space dynamically evolves.We characterize this response for a heated substrate concurrently imaged by SEM.We sys-tematically follow the evolution of pore quantity,size(length,width and cross-sectional area),orien-tation,shape(aspect ratio,roundness and solidity)and their anisotropy-interpreted by machine learning.Results indicate that heating generates new pores in both organic matter and inorganic min-erals.However,the newly formed pores are smaller than the original pores and thus reduce average lengths and widths of the bedding-parallel pore system.Conversely,the average pore lengths and widths are increased in the bedding-perpendicular direction.Besides,heating increases the cross-sectional area of pores in low-maturity oil shales,where this growth tendency fluctuates at＜300 ℃ but becomes steady at＞300 ℃.In addition,the orientation and shape of the newly-formed heating-induced pores follow the habit of the original pores and follow the initial probability distributions of pore orientation and shape.Herein,limited anisotropy is detected in pore direction and shape,indicating similar modes of evolution both bedding-parallel and bedding-normal.We propose a straightforward but robust model to describe evolution of pore system in low-maturity oil shales during heating.
查看更多>>摘要：Due to the dissimilarity among different producing layers,the influences of inter-layer interference on the production performance of a multi-layer gas reservoir are possible.However,systematic studies of inter-layer interference for tight gas reservoirs are really limited,especially for those reservoirs in the presence of water.In this work,five types of possible inter-layer interferences,including both absence and presence of water,are identified for commingled production of tight gas reservoirs.Subsequently,a series of reservoir-scale and pore-scale numerical simulations are conducted to quantify the degree of influence of each type of interference.Consistent field evidence from the Yan'an tight gas reservoir(Ordos Basin,China)is found to support the simulation results.Additionally,suggestions are proposed to mitigate the potential inter-layer interferences.The results indicate that,in the absence of water,com-mingled production is favorable in two situations:when there is a difference in physical properties and when there is a difference in the pressure system of each layer.For reservoirs with a multi-pressure system,the backflow phenomenon,which significantly influences the production performance,only occurs under extreme conditions(such as very low production rates or well shut-in periods).When water is introduced into the multi-layer system,inter-layer interference becomes nearly inevitable.Perforating both the gas-rich layer and water-rich layer for commingled production is not desirable,as it can trigger water invasion from the water-rich layer into the gas-rich layer.The gas-rich layer might also be interfered with by water from the neighboring unperforated water-rich layer,where the water might break the barrier(eg weak joint surface,cement in fractures)between the two layers and migrate into the gas-rich layer.Additionally,the gas-rich layer could possibly be interfered with by water that ac-cumulates at the bottom of the wellbore due to gravitational differentiation during shut-in operations.
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