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.

Facies-controlled prediction of dolomite reservoirs in the Middle Permian Qixia Formation in Shuangyushi,northwestern Sichuan Basin

The Middle Permian Qixia Formation in the Shuangyushi area,northwestern Sichuan Basin,develops shoal-facies dolomite reservoirs.To pinpoint promising reservoirs in the Qixia Formation,deep thin shoal-facies dolomite reservoirs were predicted using the techniques of pre-stack Kirchhoff-Q compensation for absorption,inverse Q filtering,low-to high-frequency compensation,forward modeling,and facies-controlled seismic meme inversion.The results are obtained in six aspects.First,the dolomite reservoirs mainly exist in the middle and lower parts of the second member of Qixia Formation(Qi2 Member),which coincide with the zones shoal cores are developed.Second,the forward modeling shows that the trough energy at the top and bottom of shoal core increases with increasing shoal-core thickness,and weak peak reflections are associated in the middle of shoal core.Third,five types of seismic waveform are identified through waveform analysis of seismic facies.Type-Ⅰ and Type-Ⅱ waveforms correspond to promising facies(shoal core microfacies).Fourth,vertically,two packages of thin dolomite reservoirs turn up in the sedimentary cycle of intraplatform shoal in the Qi2 Member,and the lower package is superior to the upper package in dolomite thickness,scale and lateral connectivity.Fifth,in plane,significantly controlled by sedimentary facies,dolomite reservoirs laterally distribute with consistent thickness in shoal cores at topographical highs and extend toward the break.Sixth,the promising prospects are the zones with thick dolomite reservoirs and superimposition of horstegraben structural traps.

Dissolution mechanism of a deep-buried sandstone reservoir in a deep water area:A case study from Baiyun Sag,Zhujiang River(Pearl River)Mouth Basin

Dissolution mechanism and favorable reservoir distribution prediction are the key problems restricting oil and gas exploration in deep-buried layers.In this paper,the Enping Formation and Zhuhai Formation in Baiyun Sag of South China Sea was taken as a target.Based on the thin section,scanning electron microscopy,X-ray diffraction,porosity/permeability measurement,and mercury injection,influencing factors of dissolution were examined,and a dissolution model was established.Further,high-quality reservoirs were predicted temporally and spatially.The results show that dissolved pores constituted the main space of the Paleogene sandstone reservoir.Dissolution primarily occurred in the coarse-and medium-grained sandstones in the subaerial and subaqueous distributary channels,while dissolution was limited in fine-grained sandstones and inequigranular sandstones.The main dissolved minerals were feldspar,tuffaceous matrix,and diagenetic cement.Kaolinization of feldspar and illitization of kaolinite are the main dissolution pathways,but they occur at various depths and temperatures with different geothermal gradients.Dissolution is controlled by four factors,in terms of depositional facies,source rock evolution,overpressure,and fault activities,which co-acted at the period of 23.8–13.8 Ma,and resulted into strong dissolution.Additionally,based on these factors,high-quality reservoirs of the Enping and Zhuhai formations are predicted in the northern slope,southwestern step zone,and Liuhua uplift in the Baiyun Sag.

Petrophysical parameters inversion for heavy oil reservoir based on a laboratory-calibrated frequency-variant rock-physics model

Heavy oil has high density and viscosity, and exhibits viscoelasticity. Gassmann's theory is not suitable for materials saturated with viscoelastic fluids. Directly applying such model leads to unreliable results for seismic inversion of heavy oil reservoir. To describe the viscoelastic behavior of heavy oil, we modeled the elastic properties of heavy oil with varying viscosity and frequency using the Cole-Cole-Maxwell (CCM) model. Then, we used a CCoherent Potential Approximation (CPA) instead of the Gassmann equations to account for the fluid effect, by extending the single-phase fluid condition to two-phase fluid (heavy oil and water) condition, so that partial saturation of heavy oil can be considered. This rock physics model establishes the relationship between the elastic modulus of reservoir rock and viscosity, frequency and saturation. The viscosity of the heavy oil and the elastic moduli and porosity of typical reservoir rock samples were measured in laboratory, which were used for calibration of the rock physics model. The well-calibrated frequency-variant CPA model was applied to the prediction of the P- and S-wave velocities in the seismic frequency range (1–100 Hz) and the inversion of petrophysical parameters for a heavy oil reservoir. The pre-stack inversion results of elastic parameters are improved compared with those results using the CPA model in the sonic logging frequency (∼10 kHz), or conventional rock physics model such as the Xu-Payne model. In addition, the inversion of the porosity of the reservoir was conducted with the simulated annealing method, and the result fits reasonably well with the logging curve and depicts the location of the heavy oil reservoir on the time slice. The application of the laboratory-calibrated CPA model provides better results with the velocity dispersion correction, suggesting the important role of accurate frequency dependent rock physics models in the seismic prediction of heavy oil reservoirs.

Evaluation of reservoir environment by chemical properties of reservoir water‒A case study of Chang 6 reservoir in Ansai oilfield,Ordos Basin,China

The Ordos Basin is the largest continental multi-energy mineral basin in China,which is rich in coal,oil and gas,and uranium resources.The exploitation of mineral resources is closely related to reservoir water.The chemical properties of reservoir water are very important for reservoir evaluation and are significant indicators of the sealing of reservoir oil and gas resources.Therefore,the caprock of the Chang 6 reservoir in the Yanchang Formation was evaluated.The authors tested and analyzed the chemical characteristics of water samples selected from 30 wells in the Chang 6 reservoir of Ansai Oilfield in the Ordos Basin.The results show that the Chang 6 reservoir water in Ansai Oilfield is dominated by calcium-chloride water type with a sodium chloride coefficient of generally less than 0.5.The chloride magnesium coefficients are between 33.7 and 925.5,most of which are greater than 200.The desulfurization coefficients range from 0.21 to 13.4,with an average of 2.227.The carbonate balance coefficients are mainly concentrated below 0.01,with an average of 0.008.The calcium and magnesium coefficients are between 0.08 and 0.003,with an average of 0.01.Combined with the characteristics of the four-corner layout of the reservoir water,the above results show that the graphics are basically consistent.The study indicates that the Chang 6 reservoir in Ansai Oilfield in the Ordos Basin is a favorable block for oil and gas storage with good sealing properties,great preservation conditions of oil and gas,and high pore connectivity.

Successful Lateral Predicting Technique for Reservoirs

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Reservoir prediction using pre-stack inverted elastic parameters

This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasingly used for production objectives. The pre-stack seismic property studies include not only amplitude verse offset （AVO） but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/μ, and K/μ properties, which indicate the gas characteristics in the study reservoir.

Application of Prediction Techniques in Carbonate Karst Reservoir in Tarim Basin

Carbonate karst reservoir is the emphases of Tarim＇s carbonate exploration. However, it is buried at a large depth, which results in Weak seismic reflection signal and low S/N ratio. In addition, the karst reservoir contains great heterogeneity, so reservoir prediction is very difficult. Through many years of research and exploration, we have established a suite of comprehensive evaluation technology for carbonate karst reservoir using geophysical characteristics and a geological concept model, including a technique for reconstructing the paleogeomorphology of buried hills based on a sequence framework, seismic description of the karst reservoir, and strain variant analysis for fracture estimation. The evaluation technology has been successfully applied in the Tabei and Tazhong areas, and commercial production of oil and gas has been achieved. We show the application of this technology in the Lunguxi area in North Tarim in this paper.

Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir

At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

SVM method for predicting the thickness of sandstone

The Support Vector Machine （SVM） method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method.

Seismic Prediction of Prolific Oil Zones in Carbonate Reservoirs with Extremely Low Porosity and Permeability under Salt

The Carboniferous reservoir in KJ oilfield is a carbonate reservoir with extremely low porosity and permeability and high-pressure. The reservoir has severe heterogeneity, is deeply buried, has complex master control factors, is covered with thick salt, all of which result in the serious distortion of reflection time and amplitudes under the salt, the poor seismic imaging, and the low S/N ratio and resolution. The key to developing this kind of reservoir is to correctly predict the distribution of highly profitable oil zones. In this paper we start by analyzing the master control factors, perform seismic-log calibration, optimize the seismic attributes indicating the lithofacies, karst, petrophysical properties, and fractures, and combine these results with the seismic, geology, log, oil reservoir engineering, and well data. We decompose the seismic prediction into six key areas： structural interpretation, prediction of lithofacies, karst, petrophysical properties, fractures, and then perform an integrated assessment. First, based on building the models of faults and fractures, sedimentary facies, and karst, we predict the distribution of the most favorable reservoir zones qualitatively. Then, using multi-parameter inversion and integrated multi-attribute analysis, we predict the favorable reservoir distribution quantitatively and semi-quantitatively to clarify the distribution of high-yield zones. We finally have a reliable basis for optimal selection of exploration and development targets.

Pre-stack inversion for caved carbonate reservoir prediction:A case study from Tarim Basin,China

The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.

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.

Machine learning seismic reservoir prediction method based on virtual sample generation

Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.

Prediction of Subtle Thin Gas Reservoir in the Loess Desert Area in the North of Ordos Basin

For thin gas reservoir of low-porosity and low-permeability in the loess desert area, a suite of lateral reservoir prediction techniques has been developed by Changqing Oil Company and the excellent effects achieved in exploration and exploitation in the areas such as Yulin, Wushenqi,Suligemiao, Shenmu etc., so that the Upper Paleozoic gas reserve has been stably increasing for eight years in Changqing Oilfield. The paper analyzed the effects and experience of the application of these techniques in detail.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Geological Model of Member 3 of Shahejie Formation Reservoir in Liuzan Oilfield, Eastern Hebei Province

A new geological model of Member 3 of Shahejie Formation reservoir in the Liuzan Oilfield, eastern Hebei Province was constructed by using modem reservoir modeling technology as sequence stratigraphy and conditional simulation combined with traditional geological analysis. The model consists of a stratigraphic framework model, a structural model, a sedimentary model and a reservoir model. The study shows that the reservoir is a relatively integrated nose structure, whose strata can be divided into 4 sets of parasequence, 12 parasequences. The submerged branched channel of fan delta front is the favorable microfacies, which controls the geometric shape and physical properties of reservoir sandstone. Oil is distributed in premium reservoir sandstones at structural high positions. According to the new geological model, not only the geological contradictions appearing during oil field development are resolved, but also the oil-bearing area is enlarged by 2.7km^2 and geological reserves increased by 156.9 million tons. The production capacity of the Liuzan Oilfield is increased by 0.27 million tons per year.

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.

Sedimentary Characteristics and Reservoir Prediction of Paleogene in the East Part of Kuqa Foreland Basin

Most of the Mesozoic and Cenozoic large-scale hydrocarbon-bearing basins in western China were formed in a similar foreland setting. Hydrocarbon exploration of the Kuqa foreland basin requires research into the sedimentary characteristics and filling evolution of the depositional sequences and their response to the basin process. Based on an analysis of outcrops, well logs and high resolution seismic data, the sedimentary system types and distribution characteristics of the Paleogene in the east part of Kuqa foreland basin were systematically studied. The results show that： （ 1 ） Three types of sedimentary systems are developed in the area： an oxidative salty wide shallow lacustrine system, a fan delta system and an evaporitic bordersea system. （2） The configuration and evolution of the depositional systems of the Paleogene in the Kuqa foreland basin were predominantly determined by foreland tectonism. Vertically, the Paleogene sedimentary sequence can be divided into three parts： the lower, middle and upper depositional system tracts. The lower and upper tracts commonly consist of progradational or aggradational sequences, while the middle part is usually comprised of a set of aggradational to transgressive third-order sequences. Laterally, the sedimentary systems in the east part of the Kuqa foreland basin spread from east to west as a whole, and the sedimentary facies obviously vary from south to north. The sand bodies of the delta front facies are excellent gas reservoirs, characterized by rather thick, extensive and continuous distribution, high porosity and permeability, and just a few barrier beds.

Application of Pore Evolution and Fracture Development Coupled Models in the Prediction of Reservoir "Sweet Spots" in Tight Sandstones

The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.