639 results about "Seismic attribute" patented technology

A method and apparatus for use in estimating stress fields in a geological formation are disclosed. The method includes ascertaining a perturbation of stress in the triaxial stress state in a reservoir and in a surrounding rock mass over time; and determining the change in a seismic attribute resulting from the perturbation in the stress. The apparatus includes a program storage medium encoded with instructions that, when executed by a computing device, perform such a method or a computing system programmed to perform such a method.

The invention provides a method for predicting a dolomite reservoir. The method includes acquiring seismic attributes of a target stratum, increasing the signal-to-noise ratio of the seismic attributes, adopting the seismic attributes with the signal-to-noise ratio increased to perform post-stack wave impedance inversion; adopting the seismic attributes with the signal-to-noise ratio increased to perform pre-stack elastic parameter inversion; adopting the results of the post-stack wave impedance inversion and / or the pre-stack elastic parameter inversion to predict the dolomite reservoir of the target stratum. By an effective predicting method technical system of seismic data quality evaluation, pre-stack depth processing, post-stack attribute analysis and post-stack wave impedance inversion lithologic identification, pre-stack attribute analysis and pre-stack elastic parameter inversion of a high-quality reservoir and hydrocarbon detection of the dolomite reservoir, the dolomite reservoir can be effectively predicted.

Method for aligning converted wave seismic reflection data (PS data) with conventional PP seismic reflection data so that both data types may be used to more accurately image the subsurface for hydrocarbon exploration or field development. Amplitude vs. angle (AVA) or amplitude vs. offset (AVO) attributes of PP and PS seismic data are identified and defined, which attributes are theoretically expected to be in phase and optimize seismic resolution in the data. In one embodiment of the invention, such attributes are calculated (310), then the same horizons are identified in a series of PP attributes and in a series of PS attributes, then the second series is aligned with the first at the horizon locations (316, 320), then a time transfer function is generated and applied to the PS mode data (322), and the aligned joint-mode data are inverted (326) using, for example, AVA attributes.

The invention belongs to the technical field of oil gas and coal-bed gas seismic exploration and development, and in particular relates to a reservoir sedimentary evolution analysis method in the exploration and development process. The method is used for providing reliable basic data for optimizing favorable target bed series and zone for oil gas and coal-bed gas exploration and development. In the method, sedimentary characteristics of reservoirs are analyzed by a three-dimensional seismic attribute data body, and high-resolution sequence stratigraphic principle and method, a seismic attribute analysis method and a pattern recognition method are applied; and a K-mean clustering algorithm is adopted, and the analysis process of sedimentary characteristics and sedimentary evolution of a sedimentary formation unit is performed under the control of a sedimentary isochronous interface. The method is used for assisting in reservoir sedimentary evolution research work in the exploration and development process of oil gas including unconventional oil gas, guiding the description of sedimentary characteristics of fine reservoirs of lithologic and stratigraphic trapped oil gas reservoirs, and providing the reliable basic data for optimizing the favorable target bed series and zone for the oil gas and coal-bed gas exploration and development.

A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

The present invention presents a method for determining shallow water flow risk using seismic data. The seismic data can be processed to enhance its stratigraphic resolution by sub-sampling the seismic data to less than a two-millisecond interval. Performing a stratigraphic analysis on the seismic data and evaluating the seismic attributes of the seismic data can be used to select a control location. A pre-stack waveform inversion is applied to seismic data at a selected control location to provide an elastic model, which includes pressure-wave velocity and shear-wave velocity. The shallow water flow risk is then determined using the elastic model by comparing the pressure-wave velocity to the shear-wave velocity. A post-stack inversion can be applied on the seismic data using the elastic model to model a 3D volume to determine the shallow water flow risk across the 3D volume.

A method of constructing a 3D geologically plausible velocity model for efficient and accurate prestack imaging wherein embodiments of the invention provide: (1) a method of calibrating velocity functions, appropriately and effectively taking into account well (hard) and seismic (soft) data as well as geological features, and trend fitting (“iDEPTHing”) RMS velocities before curved-ray prestack time migration; (2) a method of calibrating and trend fitting (“iDEPTHing”) interval velocities before prestack depth migration, appropriately and effectively taking into account well (hard) and seismic (soft) data as well as geological features; and (3) a method of constructing a geologically plausible velocity model using the previous steps of velocity calibration and trend fitting RMS and interval velocities, for efficient sequential use in prestack time migration followed by prestack depth migration. Advantages of the embodiments include providing a quick turnaround of prestack time and depth migration to interpreters and cutting back resource-intensive interpretation efforts for 3D seismic data The invention has significant implications for improving aspects of oil and gas exploration and production technologies, including pore pressure prediction, prospect evaluation and seismic attribute analysis.

Method for identifying geologic features, such as hydrocarbon indicators, from geophysical data, such as seismic data, by taking a curvelet transform of the data. After the curvelet representation of the data is computed (350), selected geophysical data attributes and their interdependencies are extracted (355), from which geological features may be identified (360), either from attribute data volumes that are created or directly from the curvelet representation.

Subsurface reservoir properties are predicted despite limited availability of well log and multiple seismic attribute data. The prediction is achieved by computer modeling with least square regression based on a support vector machine methodology. The computer modeling includes supervised computerized data training, cross-validation and kernel selection and parameter optimization of the support vector machine. An attributes selection technique based on cross-correlation is adopted to select most appropriate attributes used for the computerized training and prediction in the support vector machine

The invention discloses a channel sandstone reservoir identification method. The method includes: acquiring seismic response features of channel sand by performing forward modelling on reservoirs, and determining channel-sand seismic identification marks of different geologic bodies; extracting corresponding seismic attributes according to the channel sand seismic identification marks, and determining a channel boundary; under an isochronous stratigraphic frame, determining sedimentary periods of the channel sand by the aid of the stratal slice technique; determining the thickness of the channel sand according to the statistical fitting relation of the real well-drilling sand thickness and the seismic attributes. According to the method, the channel sand identification and description technology is created by going deep into seismic attribute extraction and analysis, utilizing the reservoir of the fixed well, reservoir calibration results and statistical analysis of well drilling and combining forward model analysis results. Channel sandstone reservoirs of ultra-deep layers, thin sand layers and narrow channels can be identified and described accurately, and influence of seismic resolution is less.

The invention relates to a simple multi-parameter seismic inversion method for improving the precision and the reliability of the conventional pre-stack seismic inversion with the help of the seismic wave elastic impedance, thereby simultaneously extracting a variety of formation parameters. The steps are as follows: full wave logging information is utilized; an acoustic impedance AI and an elastic wave impedance EI are determined; a CMP gather is converted into an angle gather, and part of the angles are stacked; the conventional pre-stack elastic impedance inversion process is utilized to respectively carry out the AI and the EI inversion of cross-well measuring line of small angle stacked data and part of the stacked data of the middle incident angles; the channel calculation of the EI and the AI data which are obtained by inversion is carried out, thereby obtaining the seismic attributes which are closely related to the elastic density ratio, the lithology changes and the pore fluid ingredients; the reservoir lithology and the properties of the pore fluid are determined. The simple multi-parameter seismic inversion method only needs the seismic data of near and middle angles, and other parameters are obtained through the channel calculation of the relationship between the elastic density ratio and the acoustic impedance, thereby improving the calculation efficiency and being used in the pre-stack inversion of the seismic data obtained by the vast majority of the collection conditions.

The invention relates to a high resolution sequence stratigraphic framework constraint geostatistical inversion method. The method comprises the steps that well-seismic calibration is carried out, and multi-well wavelets of an optimized target zone are extracted; based on well-seismic calibration, a high resolution sequence stratigraphic framework is established; according to target stratum seismic data, geological priori data and logging data, a geological low frequency model based on the high resolution sequence stratigraphic framework is built; sparse pulse inversion under high resolution sequence stratigraphic framework constraints is carried out to acquire a sparse pulse inversion data body; based on a meandering river geological knowledge base, target stratum logging data and seismic characteristic data are combined; the high resolution sequence stratigraphic framework constraints are used, optimized target stratum sandy land ratio and level range are selected; geostatistical inversion under the high resolution sequence stratigraphic framework constraints is carried out; based on a final geostatistical inversion result, a GR logging curve is used to carry out fine calibration on a reservoir; and seismic attributes extracted from the inversion result are used to predict reservoir sand plane distribution.

The invention provides a method for improving computing the precision of rock fracture parameters, for performance evaluation and drilling of oil and gas reservoirs in oil exploration. The method comprises the following steps of: working out fracture directions and the fracture density by using more than two seismic attributes for uniformization treatment, computing the coefficients of rank correlation of the fracture directions in pairs to obtain the rank correlation and fracture density coefficient, and if the correlation coefficient is over 0.9, determining that the density or direction is the density or direction with highest precision; otherwise, computing absolute error values of the fracture directions corresponding to all attributes at the well point, and sorting the absolute error values according to an order from small to large; computing the coefficients of rank correlation of the fracture density corresponding to the attributes and the coherency attributes, sorting the coefficients of rank correlation according to an order from small to large, and finally computing the fracture directions with the highest precision. The method can ensure the leading position of basic attributes and correct the final result to varying degrees according to the data quality so as to improve the forecast precision by using the existing information.

A method including analyzing seismic data relating to a producing hydrocarbon reservoir is disclosed. The seismic data includes first and second sets of seismic data obtained at different times. An interval composed substantially of hard rock is identified in the hydrocarbon reservoir. 4D seismic attributes for the region are calculated. Rock physics relationships are applied to seismic data related to the interval according to the permeability associated therewith. A fluid saturation change or a pressure change of the interval is inferred based on outputs of the first or second sets of rock physics relationships and the calculated 4D attributes for the interval. The inferred fluid saturation change or pressure change of the interval is outputted.

A new seismic attribute is disclosed along with its use to locate and classify seismic waveform anomalies and use them to construct objects and from them geologic surfaces (77) and bodies (75) from which hydrocarbon potential (or quality control of the seismic acquisition and processing (71)) may be assessed (79). The seismic attribute is constructed (74) from determinations of phase residues in the seismic data volume (72), preferably using complex trace analysis but alternatively by comparing neighboring waveforms for disappearing waveshape inflections. It is shown that in a data volume of the new attribute, non-zero values form strings and loops that may be associated with objects (geobodies) or surfaces such as unconformities or flooding surfaces. Methods of classification and selection (76) to reduce the number of objects generated are provided.

The embodiment of the invention provides a method for applying seismic multiattribute parameters to predicting the coal seam thickness. The method comprises: a suitable time window is selected in a three-dimensional offset data body, seismic attribute data of amplitude, frequency, and instantaneity and the like are extracted from the time window, and a seismic attribute database is established; a correlated analysis is executed on seismic attributes and coal seam thicknesses and cross-correlation analyses are further executed on the seismic attributes, so that a plurality of seismic attributes that are most meaningful are optimized as basic parameters of a coal seam thickness prediction model; with combination of known boring data, a multicomponent polynomial regression model and a BP artificial neural network model of between all the seismic attributes and the coal seam thicknesses are established by utilizing a multicomponent polynomial regression method and a BP artificial neural network method; and the models are utilized to predict coal seam thicknesses. According to the method provided in the embodiment of the invention, because multiattribute parameters are considered, obtained calculating models are perfect and realistic; an effect for prediction of the coal seam thickness is good; and credibility and accuracy are high.

A method and apparatus for estimating a porosity and a saturation in a subsurface reservoir. The method includes determining a plurality of mathematical relationships relating a plurality of fundamental physical parameters that govern elastic wave propagation in the subsurface reservoir to the porosity and the saturation in the subsurface reservoir, forward modeling a plurality of seismic attributes using the mathematical relationships to derive a plurality of conditional probability density functions for the seismic attributes, applying a Bayesian inversion to the conditional probability density functions for the seismic attributes to derive a joint probability density function for the porosity and the saturation in the subsurface reservoir, and integrating the joint probability density function for the porosity and the saturation to derive a probability density function for the porosity and a probability density function for the saturation.

The invention provides an igneous rock oil and gas exploration method and an igneous rock oil and gas exploration device. The method comprises the following steps of generating crater position and igneous rock lithofacies information according to the position of an igneous rock reservoir and characteristic information; generating geophysical reflection characteristic data according to seismic facies analysis, and determining a distribution range of advantageous lithofacies; calculating a stratigraphic strike-slip distance of a deep igneous rock mechanism according to a matching relation of target stratum lithofacies of a volcanic edifice on two sides of a fracture and a corresponding relation of igneous rocks of a middle-shallow layer on the two sides of the fracture; analyzing logging response data of the igneous rocks, and generating advantageous lithology distribution information and thickness data of the deep igneous rock mechanism through natural gamma seismic inversion; acquiring a development part of the fracture of the igneous rock reservoir by using means such as deep and shallow resistivity difference inversion; and generating igneous rock oil and gas reservoir information in the development part through cluster analysis of seismic attributes and two-phase media-based oil and gas detection. By the invention, the accuracy of exploring and predicting the igneous rock oil and gas reservoir and the drilling success rate can be improved.

This invention relates to mapping the hydrocarbon reservoir characteristics by mapping the reservoir formations that display dynamic elastic nonlinearity responses to the seismic signals. The main reason of this nonlinear behavior in the reservoir rocks is their bulk rock property: the porosity, fractures, differential pressure and pore saturation. To map these bulk rock properties, the interaction of the two seismic waves as they propagate through elastically nonlinear rocks is recorded. Two compressional seismic signals are transmitted from the surface. Seismic reflection data using surface or borehole detectors are recorded. One of the transmitted signals is a conventional swept frequency and the other is a mono-frequency signal. During the propagation of these two signals through the elastically nonlinear reservoir rocks, there is an interaction between the two signals. The sum and difference frequencies of the two primary seismic signals that were transmitted from the surface are created. These new frequencies constitute an "interaction' wave that travels along with the two primary waves. In this invention, the "interaction' signal created by the two seismic waves as they propagate through the reservoir rocks is separated from the primary signals and used to map the reservoir rocks that exhibit elastic nonlinearity due their bulk rock properties. The cross-correlation of the recorded data with the swept frequency signal provides a conventional seismic data set that is used for normal 2-D or 3-D seismic processing. The cross-correlation of the recorded data with the summed and differenced signals provides two sets of data that represent the seismic reflected image from the subsurface nonlinear formations.

The invention provides a dessert reservoir forecasting method and forecasting device. The method includes the steps: delineating the distribution range of a favorable sedimentary facies belt of a target layer based on ancient landform restoration technology of seismic sequence stratigraphic interpretation; forecasting the spreading range of main channel sand within the distribution range of the favorable sedimentary facies belt based on seismic attribute quantitative analysis technology of forward modeling; forecasting reservoir physical property distribution within the spreading range of the main channel sand based on CRP (common reflection point) gather optimization pre-stack elastic impedance inversion technology. Multiplicity caused by directly forecasting reservoir physical properties by the aid of three-dimensional seismic data in the prior art is reduced, so that low-permeability clastic rock reservoir forecasting accuracy is improved.

The invention relates to a petroleum geophysical prospecting processing technology, in particular to a method for improving the prediction precision of an oil and gas reservoir by using the seismic attribute related to offset distance. The method comprises the following steps: a CMP / CRP trace gather is transformed to an angle domain to obtain an angular-trace gather, the partial angle stacking is performed so as to form an angle stacking section, then the angle stacking section is calibrated on the scale of the reflection coefficient of the actual seismic record, and the AVO attribute RE related to the offset distance is extracted on different angle stacking sections; and an oil-gas abnormal boundary is outlined by using the extracted new attribute related to the offset distance. The invention performs gather operation only to different angle stacking sections and remarkably improves the efficiency of the attribute extraction method based on the AVO technology. In addition, the random noise is suppressed during the implementation process by stacking partial angles, and the non-determinacy of the inversional result of the AVO attribute due to the too less offset data inversed before stacking owing to the NMO correction stretching is avoided.

The invention relates to a method for identifying thin-reservoir subtle lithologic oil and gas reservoir, belonging to the technical field of petroleum exploration and development. The method disclosed by the invention comprises the following steps: calculating a quality factor of the stratum, and performing secondary high-frequency compensation on the post-stack seismic data by utilizing inverse Q filtering according to the quality factor so as to acquire ideal seismic data; performing seismic response feature research on target stratum according to the ideal seismic data, clearing the reservoir waveform features, and making a seismic data skeleton section according to the waveform reflection characters on the top and bottom surfaces of the reservoir; and finely depicting surface distribution of a sand body on the basis of the seismic data skeleton section, determining the boundary of the sand body by utilizing seismic attribution analysis so as to determine the thin-reservoir subtle lithologic oil and gas reservoir. According to the method disclosed by the invention, proved by specific examples, the subtle lithologic oil and gas reservoir can be reliably identified, and the heating identification rate of the thin-reservoir subtle lithologic oil and gas reservoir is improved.

A method of determining a fluid property in a subsurface region of interest of an earth formation uses measurements of seismic attributes on seismic data. For a test region, a plurality of realizations of rock properties are specified, and for each of the realizations and a selected value of a fluid property, the seismic attribute is modeled. This defines a probability density function (PDF). Comparison of the PDF of the model output with the PDF on the measured seismic data is used to determine the likelihood of the selected fluid property.

The invention belongs to the field of exploration geophysics, and particularly relates to a carbonatite oil and gas reservoir crack earthquake detection method. The method comprises the steps of: carrying out amplitude versus offset and azimuth angle analysis, speed versus offset and azimuth angle analysis, frequency versus offset and azimuth angle analysis respectively based on pre-stack seismic azimuth angle dataset, further carrying out ellipse fitting respectively to obtain a crack development azimuth angle, and predicting a crack development azimuth through a seismic attribute comprehensive analysis way; carrying out quality factor parameter extraction, anisotropic parameter inversion and non-hyperbolic moveout velocity analysis respectively based on pre-stack seismic trace dataset and shear wave velocity data, and predicting a reservoir crack development region by comprehensively utilizing the parameters above through a seismic attribute comprehensive analysis way. The carbonatite oil and gas reservoir crack earthquake detection method can give more comprehensive consideration to the geology of the reservoir and the physic characteristics of the globe, and the estimated crack azimuth and the predicted crack development region are more exact.

The invention concerns a method for the determination of local similarity values for geological units in the subsurface from a seismic 3-D dataset, which consists of a multitude of traces, each of which being formed by a sequence of data points that carry amplitude values, or seismic attributes derived thereof, with the following steps: Assigning a weighted environment to a respective analysis point, whereby weight factors according to a weight function are assigned to the data points in the environment of the analysis point, and calculating similarity values in each weighted environment, whereby the weight function is included in the similarity measure; and especially a method for the determination of local dip-dependent similarity values with the steps: Assigning a weighted environment to a respective analysis point, whereby weight factors according to a weight function are assigned to the data points in the environment of the analysis point, calculating similarity values in each weighted environment, whereby the weight function is included in the similarity measure, and similarity values are determined for discrete spatial orientation, and the similarity values are represented at least as a function of a dip angle and a dip azimuth; and, in each weighted environment of an analysis point, determining the maximum similarity value, which is assigned, together with the corresponding dip angle and dip azimuth, to the respective analysis point.

A method for the identification of metallic deposits in a rock formation is provided. The method for the identification includes the steps of providing an acoustic source for generating frequencies to produce acoustic waves, arranging at least one geophone optimized to detect the frequencies and oriented to enhance reflection planes from faults and fractures associated with an ore body in a rock formation and locating the acoustic source and at least one geophone in at least one shallow bore hole beneath unconsolidated surface material of the rock formation. The method further includes the steps of directing the acoustic waves to an underlying rock formation to generate seismic reflection data and processing the seismic reflection data by altering the frequency and the amplitude to identify unique seismic attributes that allow detection of at least one metallic deposit or ore body in the rock formation.

The invention relates to a method for predicting volcanic rock facies, and relates to the technical field of petroleum exploitation reservoir engineering. The method comprises the following steps: (1) forward modeling, (2) seismic attribute extraction, (3) attribute optimization, (4) volcanic rock facies classification, (5) multi-attribute neural network pattern recognition and (6) rock facies prediction, wherein in the step of forward modeling, profile forward modeling is performed on the basis of seismic geologic horizon calibration; in the step of seismic attribute extraction, horizon-oriented seismic attributes are extracted in allusion to a target horizon, and an attribute time window range is determined; in the step of attribute optimization, seismic attributes reflecting volcanic rock distribution are optimized, and preprocessing is performed; in the step of volcanic rock facies classification, category classification is performed on the volcanic rock facies according to a well drilling result; and in the step of rock facies prediction, a plurality of seismic attributes are fused according to neural network parameters acquired by training, and a distribution variation law of the volcanic rock facies in an unknown area is predicted. The prediction method provided by the invention fuses multiple seismic attributes and well drilling information, overcomes the defect that a single seismic attribute only partially reflects the lithological combination difference, and can effectively predict spatial distribution of the volcanic rock facies.

The invention discloses a seismic attribute multi-source information fusion method. By the adoption of the seismic attribute multi-source information fusion method, contradiction among multiple attributes can be solved, and a new attribute data body is generated. Compared with a single attribute comparison method, the method is more direct and more effective. Compared with a simple attribute synthesis method, the method is more accurate and scientific. According to the seismic attribute multi-source information fusion method, RPCA conversion fusion and wavelet transform fusion can be selected according to different conditions, advantages of information characteristics contained in all seismic attribute data are combined organically through the RPCA and wavelet transform, different fusion rules can be selected by the wavelet transform, comparison and analysis of fusion results obtained with different methods can be achieved, useful information in the data are fully used, the confidence coefficient is increased, the ambiguity is lowered, and the system predication reliability is improved.