Material incentives for innovative activity of enterprise personnel based on key productivity indicators of innovation

Development and implementation of innovation is one of the means of providing competitive advantages for a business in a market economy. Innovative activity of a company ought to be expedient from the economic viewpoint and result in reduction of costs and maximizing the profitability of the company’s activity in the long-term perspective. One of essential factors of economic effectiveness of innovation is innovation-active personnel able to generate ideas and quickly adapt to changes, willing and motivated to participate in implementing innovation strategy of the company’s development. Not all employees a priori possess such features, and therefore the company ought to develop and take measures to facilitate their innovative activity. The article introduces the method of material incentives for innovative activity of the personnel suggested by the authors. The method is based on adapting a well-known system of key productivity indicators to innovative activity of a company. Personnel of the company is divided into three categories of employees according to their roles in realization of the organization’s innovative policy, and the authors defined a set of four or five key productivity indicators of their activity for each category. The authors defined a range of quantitative interpretation of the achievable result for each indicator. They suggested a mechanism for calculating the total amount of the incentive bonus which takes into account key performance indicators coefficients of innovative productivity and their value, the size of the bonus wage fund for the employee, the employee’s affiliation to one of the personnel categories established, the number of rewarded employees and the frequency of premium payments. Implementation of the suggested mechanism will ensure objective assessment and reward for the contributions made by all the interested employees into innovative development of the company increasing effectiveness of the production.

Seismic Quantitative Interpretation for Uncertainty Reduction of Subsurface Modeling of the Deep Visean Reservoirs

The seismic data have historically been utilized to perform structural interpretation of the geological subsurface. Modern approaches of Quantitative Interpretation are intended to extract geologically valuable information from the seismic data. This work demonstrates how rock physics enables optimal prediction of reservoir properties from seismic derived attributes. Using a seismic-driven approach with incorporated prior geological knowledge into a probabilistic subsurface model allowed capturing uncertainty and quantifying the risk for targeting new wells in the unexplored areas. Elastic properties estimated from the acquired seismic data are influenced by the depositional environment, fluid content, and local geological trends. By applying the rock physics model, we were able to predict the elastic properties of a potential lithology away from the well control points in the subsurface whether or not it has been penetrated. Seismic amplitude variation with incident angle (AVO) and azimuth (AVAZ) jointly with rock-derived petrophysical interpretations were used for stochastical modeling to capture the reservoir distribution over the deep Visean formation. The seismic inversion was calibrated by available well log data and by traditional structural interpretation. Seismic elastic inversion results in a deep Lower Carboniferous target in the central part of the DDB are described. The fluid has minimal effect on the density and Vp. Well logs with cross-dipole acoustics are used together with wide-azimuth seismic data, processed with amplitude control. It is determined that seismic anisotropy increases in carbonate deposits. The result covers a set of lithoclasses and related probabilities: clay minerals, tight sandstones, porous sandstones, and carbonates. We analyzed the influence of maximum angles determination for elastic inversion that varied from 32.5 to 38.5 degrees. The greatest influence of the far angles selection is on the density. AI does not change significantly. Probably the 38,5 degrees provides a superior response above the carbonates. It does not seem to damage the overall AVA behavior, which result in a good density outcome, as higher angles of incidence are included. It gives a better tie to the wells for the high density layers over the interval of interest. Sand probability cube must always considered in the interpretation of the lithological classification that in many cases may be misleading (i.e. when sand and shale probabilities are very close to each other, because of small changes in elastic parameters). The authors provide an integrated holistic approach for quantitative interpretation, subsurface modeling, uncertainty evaluation, and characterization of reservoir distribution using pre-existing well logs and recently acquired seismic data. This paper underpins the previous efforts and encourages the work yet to be fulfilled on this subject. We will describe how quantitative interpretation was used for describing the reservoir, highlight values and uncertainties, and point a way forward for further improvement of the process for effective subsurface modeling.

MODERN ALGORITHMS AND SOFTWARE FOR INTERPRETATION OF RESISTIVITY LOGGING DATA

The electrodynamics of geological media investigates the interrelations of resistivity logging signals and properties of fluid-containing rocks and creates innovative well logging technologies. Its development is inextricably linked with modern techniques for mathematical modeling and quantitative interpretation of high-precision data. In order to increase the information content of galvanic and electromagnetic logging, we have developed algorithms and software for numerical simulation and inversion of field data. In our study of the Cretaceous and Jurassic deposits of West Siberia, a quantitative interpretation of high-frequency electromagnetic and lateral logging signals was carried out. To create geoelectric models, we interpreted the field resistivity logging data by an unconventional quantitative technique based on their joint numerical inversion and estimations of the vertical resistivity of permeable deposits. Another line of our research was aimed at a scientific substantiation of a new technology for mapping and spatial tracking of lateral heterogeneities and oil-promising zones in the Bazhenov Formation. The aim was achieved by using the TEM sounding data on a spatially distributed system of directional and horizontal wells.

Introduction to this special section: Quantitative interpretation

Quantitative interpretation (QI) is the geophysicist's endeavor to go beyond reservoir architecture. It is the effort to use geophysical measurements in understanding reservoir properties such as rock type, porosity, and fluid composition. QI often refers to the use of seismic amplitude analysis to predict lithology, porosity, and pore fluids away from the wellbore in oil and gas reservoirs. However, we can generalize and expand the concept of QI beyond seismic methods and beyond oil and gas reservoirs. In this special section, we feature five papers and cover not only seismic and well-log data, but also gravity and magnetic data. We address a hydrothermal reservoir in addition to several oil and gas reservoirs.

Seismic facies discrimination incorporating relative rock physics

Seismic facies discrimination is usually performed based on a rock-physics-driven quantitative interpretation approach. The accuracy of the study of rock physics largely impacts the reservoir and fluid recognition. However, the study is commonly conducted with absolute well logs without removing the trend effect. Such an approach may introduce inappropriate low-frequency information and bias further analysis of seismic data (crossplotting, facies probability density function generation, and projection angle determination). By contrast, relative rock physics with the trend decomposed reflects the rock-property variation of the overburden and underlying formation. The relative portions are more consistent with the seismic reflectivity, providing an alternative tool to facies interpretation through a seismic inversion scheme. A workflow for seismic facies discrimination has been investigated that incorporates relative rock physics, long short-term memory-based nonlinear seismic inversion, and Bayesian classification. This workflow is employed in a case study from Songliao Basin in northeast China, through which the results of relative and absolute approaches in key steps are analyzed and compared. The consistency of facies, determined through relative and absolute methods with petrophysical interpretation, is calculated. The relative analysis exhibits improved agreement with petrophysical interpretation in overall facies and reservoir sand discrimination of the blind wells. This indicates the potential to minimize the trend bias by integrating relative rock physics in quantitative interpretation.

Analyzing Gravity and Magnetic Data for the Detection of Deep-Seated Faults and Faults within the Sedimentary Cover Near Habbanieyah and Razzaza Lakes in the Middle of Iraq

This research deals with the processing and analyzing of magnetic and gravitational data for an area covering the region of Habbanieyah - Razzaza Lakes and its adjacent areas. The study includes data processing and mapping of the total gravity and magnetic anomalies for only the concerned region, then separating the residual anomalies by adopting the polynomial regression graphical method. The residual gravity anomaly reflects the variations of rock densities within the sedimentary cover. The horizontal gradient filter has been applied to the residual gravity anomaly in order to conduct the locations of fault planes within the sedimentary cover where sudden variations of gravity field take place. The quantitative interpretation for both gravity and magnetic anomalies yielded a preliminary determination for the depth to the center of major faults within the sedimentary cover. By constructing a gravity model along a profile which directed NE-SW and passing through the middle part of the study region, depth to the center of the effective faults found. This depth variation is due to the effect of tectonic activity which produced a set of faults, such faults caused the upward and downward structural motions and were responsible for positioning the deep high density causative slabs of bedrock. The residual magnetic field quantitative interpretation along two profiles crosses over anomalies at the NE and SW parts of the region yielded the depth to the top of magnetized basement rocks. The difference in depth of the basement rocks and the shifted anomaly locations reflects the effect of tectonic activity which may relate to a strike slip faulting in the higher depths.

Seismic Forward Modeling of Semberah Fluviodeltaic Reservoir

Semberah field’s infill drilling activity to increase its recovery has been generally challenging because of limited seismic information to support the reservoir distribution characterization. Stratigraphic model building has been using mainly geological concept and well log analysis while undermines seismic information because of poor quality 2D lines. The best seismic quantitative interpretation uses in Semberah encompass amplitude mapping of extracted post-stack attributes. Semberah asset team recently suggests a new stratigraphic framework consists of isolated distributary sands and active delta switching sequences. The new framework allows seismic forward modeling method to constrain the sand boundaries. The seismic modeling workflow involves building rock physics models, performing synthetic modeling of varying channel facies over its elastic properties. The synthetic PP-reflectivity generation uses Semberah well’s wavelet extraction from Roy-White algorithm extraction which are later varied with several scenarios of fluid, porosity and random noise. The latest volumetric estimation from the integrated modeling produces significant oil and gas resources to justify Semberah further development. Both static model and seismic forward modeling suggest potentially finding wet sands during the SB-27 well drilling activities in July 2019. The well’s location uncertainty has been optimized by moving the well location to a structurally updip position from the existing well UKM-03 to avoid potential water level. A recommendation has also been put forward for the remaining five-well drilling proposals to sharpen the targeted stacked channels around the recommended areas. The seismic forward modeling technique has never been applied as part of the seismic quantitative interpretation method in Semberah, yet such process could be carried out with only 2D seismic lines. The result from seismic forward modeling provides better integration with the geological model and becomes a cost-effective option to optimize area with limited dataset such as Semberah. The updated geocellular model and the seismic forward modeling results have already been used to identify a number of prospect area and would invigorate the future Semberah well drilling proposals.