About us
IPLab LLC is a software development company, resident of Skolkovo Innovation Center 
in Moscow, Russia, focusing on development of software products for prediction of oil and gas formations productivity parameters.
We solve the following tasks, using machine learning algorithms and next-generation Kolmogorov neural networks:
Analysis of the seismic field without a teacher (without wells)
- Building maps of seismofaces (seismic classes) based on classification applying 1D,2D or 3D Kohonen neural networks with RGB visualization.
- Selection of faults and fractures based on the DTW algorithm
- Tracing of faults based on the new algorithm of simulation of faults and disturbances with the use of multiple local stresses
- Selection of factors based on the orthogonal decomposition of the seismic field.
- Selection of seismic field features based on RGB images of adjacent tratigraphic slices of the seismic field or attributes.
Analysis of the seismic field with a teacher (teacher - information from wells)
Forecast of effective thickness maps.
- based on linear regression,
- based on non-linear ACE regression,
- based on Random Forest nonlinear regression,
- based on Kohonen neural networks
- on the basis of classical neural networks
- based on new generation Kolmogorov neural networks
- multiple forecast with removal of part of wells and construction of maps P10, P50, P90, average, standard
Forecast the cube of effective parameters based on a set of source cubes and well measurements.
- based on linear regressions,
- based on classical neural networks
- based on new generation Kolmogorov neural networks
- Neural network forecast of the low-frequency model and its application.
- Nonlinear prediction of cubes of elastic parameters AI, Vp/Vs, RHOB by angular sums
- Forecast of FES cubes by inversion cubes or angular sums
- Forecast of geomechanical cubes (speed Vs, young's modulus, Poisson ratio ...)
- Pore pressure cube forecast
- Forecast of lithofacies cubes
- multiple forecast with the removal of part of wells and construction of cubes P10, P50, P90, average, standard
Analysis of well data.
Forecast of curves (core parameters) for a set of logs
- based on linear regression,
- based on classical neural networks
- based on new generation Kolmogorov neural networks
- multiple forecast with the removal of part of wells and construction of curves P10, P50, P90, average, standard
Classification of logging curves according to their shape-electrofacing
IPLab software is based on:
- A new generation of Neural networks based on Kolmogorov full function neurons with innovative methods of hybrid training.
- Seismic inversion applying full, angle and azimuth stacks.
- Fracture analysis based on seismic data set.
We use modern innovative machine learning algorithms for data processing and interpretation of complex data of different scale and with different accuracy (well test, seismic, ground studies, aerospace data). Development of technologies based on these algorithms involves the use of large amounts of input data in order to build reliable forecasts reservoir productivity for conventional and unconventional hydrocarbon deposits.
As the basic algorithm for production prediction, neural network with hybrid training approch is used. It will provide a high degree of freedom nonlinear prediction operator. Training of neural networks and their stabilization is based on a combination of genetic algorithms, gradient methods and regularization.
In addition, we applied our ideas on seismic inversion based on 'direct' inversion constructions in the frequency domain for full, angular and azimuthal stacks.
In order to detect the fracture zones and hidden faults on the 3D seismic data we make use of innovative artificial intelligence algorithms.