• 2D/3D data processing in the time domain

    Noise of any nature attenuation (LIFT - Leading Intelligent Filter Technology)
    New software package in Focus system

    LIFT (Leading Intelligent Filter Technology) suppresses low-velocity surface, industrial and high-amplitude noise waves. LIFT provides suppression of various noises in various frequency ranges while maintaining amplitudes and phase characteristics of the signal. LIFT is a method based on modeling signal and noise, and then suppressing random and coherent noise in a non-linear adaptive way. The main tools for completing the task were the following procedures:

    • - median filtering for high-amplitude noise attenuation;
    • - multichannel linear noise filtering in various frequency ranges.

    The advantage of this complex is the ability to attenuate noise without procedures that introduce significant amplitude distortion (for example FK-filtering, etc.) LIFT (Leading Intelligent Filter Technology) can be used at various stages of 2D/3D seismic data processing and has no restrictions on the number of iterations. It should be noted that the greatest difference in results is obseved at nearest offsets. Thus, LIFT technology allows you to subtract multiple waves where there is no difference in kinematic corrections.

    LIFT technology efficiency examples (Leading Intelligent Filter Technology)

    2D/3D seismic data reprocessing examples

  • 2D/3D data processing in the depth domain

    Depth-velocity model building

      Depth-velocity model of an environment:
    • - isotropic pre-stack depth migration using GRID tomography;
    • - anisotropic pre-stack depth migration using VTI tomography.

      Application of various depth migration algorithms:
    • - Kirchhoff's algorithm;
    • - full-wave algorithm (with preservation of true amplitudes);
    • - angular algorithm in the frequency domain;
    • - EarthStudy 360 algorithm.

      Tasks:
    • - building of a depth-velocity model of the environment, which describes the geological structure in a given area;
    • - the use of tomography to define heterogeneities in the velocity model;
    • - the use of a full-wave algorithm of the depth migration, which will significantly expand the frequency range of seismic data in the depth domain.

    Equations of the full-wave migration, including Phase Shift (WEM) and reverse wave migration (RTM) are designed to solve various problems, and provide more accurate images of horizons. In addition, full-wave migration works more correctly with complex interval velocities that describe the geological situation of a given region and produces images with less background or operator noise than those created with Kirchhoff migration.

    Control of the velocity model on the traveltime curve alignment, the use of tomography

    Application of various depth migration algorithms

  • 2D/3D seismic data interpretation

    • Interpretation in time and depth domain

      Correlation of reflecting horizons and fault zones with reference to well logging data

      The company has vast experience in 2D/3D seismic data interpretation of any complexity with other geological and geophysical study at objects located in different geological conditions (the Caspian Basin, Mangyshlak, Northwestern Aral, etc.)

      A field geological structure model is created after all geological and geophysical data analuzing (well logging, core analysis, well test results, field production, geophysical methods). The created model indicates the most prospective objects for drilling.

      Volumetric model of the structure located in the overhang

      Modelling of the deviated well

      Identification of paleochannel bodies with definition characteristics of the volume of gas-oil-saturated sandstones

      Identification of gas deposits in the Paleogene

    • Seismostratigraphic analysis

      Seismic sections reflecting the clinoform structure productive formation in the Paleogene

      The accumulation of sediments, in which hydrocarbons may appear, occured under certain physco-geographical conditions. The distribution of sedimentary rocks in time and space largely determines the size and shape of natural oil and gas reservoirs, and, consequently, the reserves of these minerals. In this regard, knowledge of the general and particular patterns of formation of sedimentary strata is of significant practical importance.

      The regular alternation of rock complexes makes it possible to judge the periodic change of sedimentation conditions and the general direction of change in these conditions in different periods.

      Below are examples of seismic sections, where, based on the results of sequence-stratigraphic interpretation of seismic data and detailed correlation of well logs materials, it was established that gas-bearing deposits of a given area have a clinoform structure and require an individual approach to geological modeling.

    • Integration of Geophysical Methods

      Comparison of gravimetric and wave fields (3D data)

      One of the priorities of our company is the integration of seismic data with other geophysical methods, in particular with a gravimetric field. Despite the high informativeness and effectiveness of modern 3D seismic survey, difficulties often arise in the geological interpretation of the Caspian Basin wave field, caused by the complexity of stratification of reflecting horizons. This primarily relates to tracking the roof of salt sediments. Due to the anomalously reduced density of the salt domes, the gravitational field are displayed by intense minima, which makes it possible to use gravity data together with seismic data not only to identify such structures, but also to study their morphology, volumetric modeling of salt core and salt density. Integration of geophysical methods with other geophysical methods allows both confirming and identifying new prospective structures for hydrocarbon.

      Identifying an overhang on the 2D data based on comparison of the gravimetric field and the wave field

  • Dynamic processing of seismic data

    • Calculation and analysis of the coherence cube

      Paleochannel on the coherence cube

      Using the “Coherence Cube” technology, tracing and mapping of various zones of coherence in a seismic wave field is carried out, which is associated with the faults in the geological section, which are difficult or impossible to visually identify, depending on the fault amplitudes. At the same time, some processing procedures tend to “smooth” the areas of signal coherence worsening when tracking reflections, which leads to the loss of images of faults, especially lowamplitude ones, on ordinary time or depth cubes.

      Identifying of the stratigraphic objects on the coherence cube

      Calculation of the coherence cube on the post stack data allows to trace and map different zones of coherence faults in a seismic wave field, which is associated with the presence of faults in the geological section, as well as identify and clarify the location of various geological bodies, such as, for example paleochannels which are important signs when hydrocarbon fields discovery.

    • Seismic facies analysis

      One of the most popular methods for seismic data integrated geological interpretation is a seismic facies analysis. The Stratimagic soft provides classification and interpretation of seismic facies on waveform using neural network technology for both 3D and 2D seismic data.

      Seismic Facies Analysis (horizon map) in combination with well data

      Examples of seismic facies 2D sections

      Seismic facie combines a group of reflections characterized by a similar set of parameters, such as configuration, continuity, amplitude, frequency, etc. Each parameter carries certain information about the geological structure of the studied interval.

      The configuration of the reflections is closely related to the main characteristics of the formation, the continuity of the reflections - to the continuity of the layers, the amplitude shows the ratio of density and velocity, the frequency depends on the thickness of the layers, etc. The results of seismic facies analysis are reliable basis for object modeling, as well as for predicting the filtration-volumetric characteristics of the prospective structures.

      A joint analysis of well logs and seismic facies allows predicting a geological section with a higher probability.

      Seismic facies with sandstone reservoirs

      The allocation of the seismic facies along the horizon in a 3D model

    • AVO Analysis

      CDP gathers before and after additional processing and its quality characteristics

      Dynamic analysis using the AVO method (Amplitude Variation with Offset) studies the dependence of the amplitudes of the reflected waves on the distance or angle of wave incidence on the reflecting boundary. AVO is a seismic method that determines the fluid content in rocks, porosity, density, seismic velocity, shear waves information and other fluid indicators. This method has been successfully used to predict hydrocarbon reservoirs (especially gas) and lithological structure.

      Anomalies of AVO on a crossplot between Normal Incidence Reflectivity and Fluid Factor

      Anomalies of AVO on a crossplot between Fluid Factor and Gradient attributes

      In comparison with the “bright spot" method, AVO analysis is performed not according to time sections, but according to CDP gathers after time migration and calculated attributes, which assumes the presence of high-quality field data (signal-to-noise ratio should be higher than 1O). Before starting the analysis, additional processing is carried out in order to increase the resolution and weaken regular and irregular noise.

      The basis for the AVO anomalies analysis and their interpretation is the comparison of AVO attributes (Normal incidence Reflectivity, Gradient, Fluid Factor, etc.) and their statistical analysis in the crossplot field. Hydrocarbon prediction is carried out by identifying the background trend and zones associated with the gas-saturated part of the reservoir on the crossplot and marking these zones back to attribute sections (surfaces).

      Anomalies of AVO in the overhang structure

    • Seismic inversion

      Preparing seismograms for inversion

      The method wave field seismic inversion is a method of mathematical seismic modeling that integrates a dynamic interpretation of seismic data with the results of drilling, as well as detailed studies using well logging methods. To date, this method seems to be the most effective for solving problems of predicting a geological section. The software used by our company allows to receive seismic inversion data in various ways, depending on the task and the available data. Thus, the inversion process can be performed for 2D / 3D seismic data both on the prestack gathers and on the cube (section).

      Input data for inversion: CDP gather, shear wave velocity, density and primary wave velocity.

      The most effective and preferred method is the inversion of migrated gathers based on the maximum likelihood algorithm (PML-inversion). The main features and advantages of this method:

      • - inverts each trace of CDP gathers;
      • - uses migrated offset and angle gathers of any type at the input, and any angular sums as well;
      • - allows to receive not only P-impedance and S-impedance, but also other cubes of rock properties: Vp, Vs, VpNs, Poisson’s coefficients, LR, MR and synthetic seismograms.

      Seismogram preprocessing, as in AVO, is performed during the inversion process, taking into account the effect of signal stretching along the offset for each trace. The input data for this inversion are background models of impedance or velocities of primary and shear waves and density, created by kriging interpolation of well curves defined by the surfaces of reflecting horizons.

      Maps of P-impedance and S-impedance with a plotted anomalies, obtained from a crossplot with a separation of oil- and water-saturated reservoirs. Boundary conditions are tied with well log data

      Map of Vp/Vs with a plotted structures and anomalies, obtained from a crossplot with a separation of oil- and water-saturated reservoirs. Boundary conditions are tied with well log data (less than 1.9)

      Separation of presalt complex into carbonate and terrigenous sediments acoording to the inversion result. Carbonates are distinguished by high primary wave impedance values (red)

      Crossplot between PHIE (porosity) and Vp/Vs Ratio sections

      Crossplot between Vp/Vs Ratio and Poisson's Ratio (horizontal sections)

      Cross sections of acoustic impedance (P-impedance) and porosity (PHIE) along the traverse line. Comparison of well logging data and seismic data.

      Crossplot between Vp/Vs and Poisson's Ratio (horizontal sections)

      Crossplot between P-wave Velocity and S-wave Velocity (horizontal sections)

      Comparison of well logging data with curves extracted from calculated as a result of PMLI inversion of attribute cubes

      An example of the results of PMLI-inversion for P- and S-waves. The following attribute cubes are the output:

      1. P-wave Impedance;
      2. S-wave Impedance;
      3. P-wave Velocity;
      4. S-wave Velocity;
      5. Vp/Vs - the ratio of P and S velocities;
      6. Poisson Ratio;
      7. Mu*Rho - product of Shear modulus and density;
      8. Lambda*Rho - P-wave derived parameter known as "incompressibility.

      Fragment of the slice of the Vp/Vs attribute (in the 3D window) along the productive zones in Cretaceous deposits

      Vp/Vs section with anomalies obtained from crossplot with the separation of oil-saturated reservoirs. Boundary conditions are tied with well log data

      Well logging data modeling based on the Gassmann equation with the substitution of water, oil and gas reservoirs

      The physical properties of the rocks are used to determine the relationship between the elastic properties of rocks (elastic modulus) and the observed seismic responses (velocities). One of the rock physics studies is fluid-to-gas-fluid substitution, which is used to model and quantify various fluid-bearing phenomena in order to evaluate the possible exploration of pore fliuid at seismic velocities.

      General form of the Gassmann equation:

      • Kdry - Effective bulk modulus of dry rock;
      • Ksat - Effective bulk modulus of saturated rock;
      • Kg - Bulk modulus of the mineral grains constituting the rock;
      • Kf - Effective bulk modulus of pore fluid;
      • φ - Porosity

  • Well logging data interpretation

    Reservoirs identifying on litology, GR, RHOB, DT, Vp/Vs

    A complex interpretation of the well logging data is the basis for determining the physical properties of the section and compiling a geological model for the studied object. Before starting work, all the curves are aligned and normalized for further work. As a result of the well logging data interpretation, the following parameters are determined:

    • - shale volume;
    • - porosity according to acoustic logging;
    • - porosity according to a complex of density and neutron logs;
    • - water saturation;
    • - oil and gas saturation, etc.

    Comparison of porosity, saturation and permeability coefficients according to well logging (Phi, Swe, Repm) and core data (Phi core, Swe core, Perm core )

    The obtained parameters are compared with cor analysis data. Evaluation of differences in the acoustic characteristics of rocks in reservoirs and non-reservoirs is determined on the basis of the well logging interpretation and is the basis for inversion transformations of the wave field. Comparison of reservoir properties from well logging and seismic data is an important step in creating a geological model of the seam.

    Comparison example of a porosity curve calculated using well logging data (PHIE_LOG) and a curve calculated from the dependance of P-Impedance from PHIE (PHIE_IMP)

    Analysis of lithology according to well logging data (seismic section on the traverse along the wellbore)

    Modeling of Shear Wave Velocity (Vs) Curve

    Calculating shear wave velocity values is very important for calculating PMLI inversion. The Castagna relationship allows to convert the pressure wave velocity into the shear wave velocity:

    Vs=0.862*Vp-1.172 (km/sec)

    (formula is applied to obtain Vs in host rocks)

    Below is an example of modeling a Vs curve in a well with a gas reservoir based on an observed curve in a well from an adjacent area.

    Use of Well Logging Data in Facies Analysis of The Section

    The predominance of coreless drilling, the development of new methods and technologies for recording, processing and interpreting geophysical materials have led to the widespread use of various geophysical methods for interpreting the genesis of sedimentary rocks.

    A detailed study of well sections makes it possible to judge facies variability, changes in the thickness of each individual layer or layer pack, conditions of occurrence of layers, etc.

    Lithological subdivision of well sections can be carried out according to the configuration of well logs. Knowing the lithological characteristics of the section, it is possible to characterize it's facies.

    Various facies log models represent log curves of a specific shape. The type of facies is established by comparing the behavior of the logging curve opposite the formation with typical forms of logging curves for various types of facies. Facies logging models reflect characteristic changes in certain geological parameters (for example, clay content, porosity, etc.), estimated from well logging results for various types of facies along the section

    Subsequent careful consideration of well logging data with genetic limitations, identification of facies and cyclites of different orders, their tracking along the section and area allows us to achieve a more reliable correlation of different facies deposits, and therefore, more reasonably compare and connect productive strata - oil and gas reservoirs.

    Facies analysis in the well according to logging data

  • Pore pressure prediction

    Pore pressure volume (psi)

    Eaton Equation calculates excess pressure resulting from insufficient rock compaction:

    PP=OBP-(OBP-HP)*(Vmeasured/Vnormal)3,

    • where:
    • - PP – pore pressure;
    • - OBP – rock pressure (under the logging data);
    • - HP - hydrostatic pressure (calculated);
    • - V(measured) - seismic interval velocity with tomography;
    • - V(normal) - interval velocity at standard compaction law (logging data).

    Hydrostatic pressure, exerted by the equivalent liquid on the column is determined by the relation:

    HP(z)=0.4335*p(fluid)*Z,

    • where:
    • - HP - hydrostatic pressure (calculated) in psi;
    • - Z - depth in feet;
    • - p(fluid) - density of liquid in g/cc;
    • - estimated density of water is 1.03 g/cc.

    Rock pressure (ОВР), exerting from the overburden, is extracted from the density curves and displayed as a trend:

    Normal compaction trend is calculated from acoustic velocity curves:

    Interval velocities are derived from seismic velocities and calibrated using well data:

    Resulting interval velocities after “Constrained (Dix) Velocity Inversion”

    Well Interval Velocity Calibration. A constant calibration factor of 1.05 is applied due to poor connection between seismic and borehole velocities:

    Pore pressure obtained by Eaton equation: