**IPLAB Solutions**

IPLab offers its clients a range of software products for the purposes of prediction of oil and gas formations productivity parameters. IPLab software can be tailored as Petrel plug-ins or as separate modules with the possibility of importing and exporting of data compatible with Petrel.

Currently, we offer potential clients a free trial of our modules in order to give them an opportunity of testing the IPLab software products for the first project at no cost in order to demonstrate the effectiveness of our solutions.

**Available Modules**

**I/O Modules**

**SEGY loader **– seismic data input in SEGY format,

**LAS loader **– input of borehole data in LAS,

**Excel wells loader **- input of well data in Excel format,

**Excel points attribute loader **- input of spatial point data with attributes in Excel format,

**ASCII points loader **- input of spatial point data with attributes in ASCII format,

**Surface ASCII loader **- input of 2D grid data with attributes in ASCII format,

**XYZ loader **- input of spatial mesh data with attributes in XYZ format,

**Well Marker loader **- input of markers data on wells in Petrel ASCII format,

**Time Depth log loader **- input of data "time-depth" on wells in ASCII format,

**SEGY export **– output of seismic data in SEGY format,

**Surface export **– output of 2D grid data with attributes,

**Points export **– output data points with attributes.

**Modules for working with 2D attributes (with a set of maps) **

**Maсhine Learning surface property prediction **– module for predicting effective parameters by a set of 2D attributes (a set of maps) based on linear regression, regression based on neural networks, ACE regression, Random Forest regression, Nearest Neighbor regression, regression based on neural networks with arbitrary activation functions.

** At the input **– there are several maps with seismic attributes (for example, slicing amplitudes in the layer) and a set of intersection points with the predictive parameter (effective thickness, average porosity, cumulative production, ...)

** The result **– several maps with average forecasts, P10, P50, P90 and the forecast spread (standard deviation).

**Surface_Factor_Analysis **– a module for calculating independent factors by a set of 2D attributes (a set of maps) based on 1) Autoencoder technology - a "narrow neck"; and 2) PCA (Principal Component Analysis). It can be used to identify fracture zones.

** At the input **– there are several maps with seismic attributes (slicing amplitudes in the layer)

** The result **– several cards with independent components (factors).

**SOM classification **– a module for classifying a set of maps with a sliding window based on the self-organized mapping by Kohonen algorithm using 1D, 2D and 3D configurations of Kononen neural networks.

** At the input **– there are several maps with seismic attributes (slicing amplitudes in the layer)

** The result** – one or more maps with classification results.

**Calculator for surface properties **– a multi-line calculator based on c # with access to data in a sliding window

** At the input **– there are several maps with seismic attributes (slicing amplitudes in the layer)

** The result **– one card with the results of calculations.

**Modules for working with seismic cubes **

*As a result, the modules working with seismic cubes allow you to get virtual cubes. Such cubes retain only the computation algorithm and appear instantly. When visualizing on a cut, only those traces that are close to the cut are computed and therefore, it results in a possibility to visualize the result very quickly, without computing the entire cube. It also makes possible to correct “on-the-fly” the calculation parameters with fast visualization, which allows for a quick selection of parameters on a section with visualization of borehole curves. *

**Maсhine learning cube prediction **– a module for predicting effective parameters by a set of seismic cubes (full, angular amounts) based on linear regression, regression based on neural networks, Random Forest regression, regression based on neural networks with arbitrary activation functions.

** At the input **- one or several cubes (angular and azimuthal sums, any cubes with attributes) and several wells with predicted curves. It is possible to specify the roof and the bottom of the layer to limit the forecast.

** The result **– virtual cubes with forecast parameters, average forecasts, P10, P50, P90 and forecast spread (standard deviation).

**Poststack seismic inversion **– a module of inversion constructions for estimating an acoustic impedance based on classical inversion algorithms using a seismic pulse or on the extraction of a statistical pulse separately for each trace. The module also includes

** At the input **– there is one seismic cube (full amount). The pulse is automatically extracted from each trace.

** The result **– a virtual cube with AI, resulting in a quick change of parameters.

**AVO_seismic inversion **– a module of inversion constructions based on the joint application of a set of corner cubes before summation when calculating AI, Vp / Vs, density on the basis of Aki and Richards equations with calculation in the frequency domain.

** At the input **- a set of corner cubes, the pulse is extracted automatically from each trace. It is possible to specify a low-frequency model.

** The result **– virtual cubes with AI, Vp / Vs, density with the ability to change parameters quickly.

**AVOAZ_seismic inversion **– a module of inversion based on the joint use of a set of angular and azimuth cubes before summation aiming at calculation of AI, Vp / Vs, density, and the azimuthal heterogeneity indices? with calculation in the spectral domain.** Results **– virtual cubes with AI, Vp / Vs, density, and the indices of azimuthal density heterogeneity with the possibility of operative parameter change.** (Only for testing - not ready for commercial use) **

**Seismic_Factor_Analysis **– module for calculating independent factors for a set of 3D attributes (set of cubes) based on technology 1) Autoencoder - "narrow neck"; and 2) the method PCA (Principal Component Analysis). It can be used to identify fracture zones.** The result **– is several virtual cubes with orthogonal components.

**Cubes classification **– a 3D classification module for a set of seismic cubes based on the self-organized map (SOM) algorithm using 1D, 2D and 3D configurations of Kononen neural networks and RGB visualization of results. **The result **- is virtual cubes with classification results

**Seismic facies analysis **– a module for seismic facets in the form of a seismic signal in the layer under study, which uses a volumetric signal (sub-cube for several traces) for classification. As an algorithm for classification, the SOM (self-organized map) algorithm using 1D, 2D and 3D configurations of Kononen neural networks and RGB visualization of results is used.

**The result **– a set of maps with classification results.

**Calculator for cubes **– a multi-line calculator based on c # with access to data in a sliding window (an example of a procedure for computing a cube of coherence and calculating cubes for spectral decomposition is included). **The result **– virtual cube with the results of the calculation.

**Calculator for well logs **– a multi-line calculator based on c # for calculating new curves on a well with access to data in a sliding window.** The result **– a new curve with the results of the calculation.

**Modules of work with point data **

**Maсhine Learning cube prediction **– a module for predicting effective parameters for a set of attributes based on linear regression, regression based on neural networks, Random Forest regression, regression based on neural networks with arbitrary activation functions.** The result **– a new attribute with predictive parameters.

**Modules for working with gravimagnetic data **

**Layers gravy-mag modeling and inversion **– module for modeling and inversion of gravimagnetic data on the basis of 3D layered model. The inversion allows you to adjust the density or position of the boundaries of layers, taking into account the constraint model.

**Express gravy-mag modeling and inversion - **a module for modeling and inversion of gravimagnetic data based on a 3D model in the form of a cube SEGY.

**Visualization**

*Visualization modules include windows with visualization of maps, sections along lines or cross lines. Модули визуализации включают окна с визуализацией карт, разрезов по линиям или по кросс линиям. *

*Fig. 1. Example of the IP_Seismic software interface.*

*Fig. 2. An example of a seismic section is the original (above) and aligned to a given horizon (below).*

*There is a visualization of RGB maps, and RGB cross-section (mixing colors of red, green and blue for three cubes (real or virtual) or three cards.*

*Fig 3. Example of RGB map.*

*Fig. 4. An example of RGB cross-section.*

*There is the possibility of drawing plots (cross rafts) and histograms.*

** **

*Fig. 5. An example of cross-rafts.*

*To visualize the well data, there is a special window (well section).*