Background. The article considers the results of updating the geological model of the khamakinskii horizon reservoirs of the Chayandinskoe oid and gas field. The main aim is project the production of the oil rims and form a positive business case of the project.

Materials and methods. Conceptual sedimentary model bases on the core of the 14 wells. Updating of the petrophysical model is the key to identify post-sedimentary transformations (like anhydritization and halitization) and the opportunity to correct the permeability trend. The tectonic pattern of the horizon based on the interpretation of 3D seismic data. There are two groups of faults were identified: certain and possible. Neural networks algorithm uses for a creating the predictive maps of anhydritization, which are used in the geological model.

Results. Estuary sands influenced by fluvial and tidal processes dominate the khamakinskii horizon. The reservoir is irregular vertically: at the base of the horizon, there are sandstones of the delta front and there are alluvial valley with fluvial channels in the middle and upper parts. Eustary sands eroded by incised valleys (alluvial channels). According to the core and thin section analysis, the main uncertainty is sedimentary transformations of reservoir. It affects the net thickness and then the volume of oil in productive wells. 3D geological model includes the trends of anhydritization and halitization over the area, which makes it possible to obtain a more accurate production forecast.

Conclusion. As part of the probability estimate of oil reserves, the main geological parameters that affect the volume of reserves were identified. Pilot project is planning to remove geological and technical uncertainties.

Background. The article presents a new approach to assessing the geological complexity — quantitative assessment of areal complexity, as well as an alternative methodology for assessing complexity in 1D.

Aim. Developing a numerical metric for assessing the geological complexity and creating an algorithm for complexity maps construction.

Materials and methods. Generally, complexity describe the reservoir in one number, that often underestimates the real complexity of the deposit. Geological complexity, presented in the article consists of 5 groups: structuraltectonic, facies-lithological, permeability and porosity, secondary alteration and fluid properties, 13 characteristics describe the complexity space of these groups. Each of these characteristics could be presented not only in 1D but also in 2D. The proposed methodology was tested on the company’s assets.

Results. The presented examples of complexity maps for several fields show the advantage of 2D complexity estimation in comparison with 1D. The analysis of decomposed complexity estimation (for individual groups) on the company’s assets showed that the key groups of complexity are structural-tectonic, facies-lithological characteristics. Therefore, characteristics that describe these groups should be taken into account during the decision-making process and assets ranking.

Conclusion. A methodology of quantitative assessment of areal geological complexity has been developed. This areal assessment allows identify the most “problematic” areas, analyzing existing sources of uncertainty, and also ranking and screening company assets when making strategic decisions.

The goal of carbonate rock typing is a realistic distribution of well data in a 3D model and the distribution of the corresponding rock types, on which the volume of hydrocarbon reserves and the dynamic characteristics of the flow will depend.

Common rock typing approaches for carbonate rocks are based on texture, pore classification, electrofacies, or flow unit localization (FZI) and are often misleading because they based on sedimentation processes or mathematical justification. As a result, the identified rock types may poorly reflect the real distribution of reservoir rock characteristics.

Materials and methods. The approach described in the work allows to eliminate such effects by identifying integrated rock types that control the static properties and dynamic behavior of the reservoir, while optimally linking with geological characteristics (diagenetic transformations, sedimentation features, as well as their union effect) and petrophysical characteristics (reservoir properties, relationship between the porosity and permeability, water saturation, radius of pore channels and others). The integrated algorithm consists of 8 steps, allowing the output to obtain rock-types in the maximum possible way connecting together all the characteristics of the rock, available initial information. The first test in the Middle East field confirmed the applicability of this technique.

Results. The result of the work was the creation of a software product (certificate of state registration of the computer program “Lucia”, registration number 2021612075 dated 02/11/2021), which allows automating the process of identifying rock types in order to quickly select the most optimal method, as well as the possibility of their integration. As part of the product, machine learning technologies were introduced to predict rock types based on well logs in intervals not covered by coring studies, as well as in wells in which there is no coring.

Background and aim. The complexity of the structures of the Paleozoic deposits of Western Siberia requires the use of specialized methods for seismic data processing. However, the standard time processing procedures are still used in Western Siberia. Therefore, in this work, the goal is to study of seismic processing procedures for the construction of high-quality images of the pre-Jurassic complex in Western Siberia.

Materials and methods. A comparative analysis of time and depth processing was carried out in the paper on realistic synthetic data and models from Western Siberia containing the pre-Jurassic complex. Numerical examples are calculated for synthetic data obtained from two realistic seismic models. To create the first model, various geological and geophysical data from the Tomsk region are used. The most difficult areas of the Paleozoic in this model are steeply dipping carbonate structures and intrusive formations with steep slopes and outcropping to the erosion surface. Another model was built based on the seismic data processing results in the area of the Maloichskoye and Verkh-Tarskoye fields in the Novosibirsk region. Based on these data, the main horizons and a system of sub-vertical faults, characteristic of the pre-Jurassic deposits of the Novosibirsk region, were identified. Seismic data processing was carried out with an emphasis on the possibility of object-oriented migration.

Results. It is shown that the time processing of seismic data is insufficient and the need for deep processing to construct kinematically correct images of pre-Jurassic deposits. We also compared migration algorithms based on Gaussian beams and found that object-oriented migration gives the best quality results.

The main goal of the paper is demonstration of permanent downhole long-term monitoring capabilities for oil and gas production profile along horizontal wellbore in case of natural flow. The informational basis of the results obtained is the data of long-term temperature and acoustic monitoring in the borehole using a distributed fiber-optic sensor (DTS + DAS).

Materials and methods. At the same time, flowing bottom-hole pressure and surface rates were monitored at the well for rate transient analysis, as well as acoustic cross-well interference testing [1], based on the results of which “well-reservoir” system properties were evaluated, the cross-well reservoir properties of the were estimated, and the possibility of cross-well testing using downhole DTS-DAS equipment was justified.

The research results made it possible to assess reliability of DTS-DAS long-term monitoring analysis results in case of multiphase inflow and multiphase wellbore content. In particular, DTS-DAS results was strongly affected by the phase segregation in the near-wellbore zone of the formation.

Conclusions. In the process of study, the tasks of inflow profile for each fluid phase evaluation, as well as its changes during the well production, were solved. The reservoir intervals with dominantly gas production have been reliably revealed, and the distribution of production along the wellbore has been quantified for time periods at the start of production and after production stabilization.

Background. Predicting the dynamics of the Bazhenov formation is an important task. Traditionally, it is carried out using geological and hydrodynamic modeling, i. e., solving the direct problem of hydrodynamics.

However, for shale reservoirs, this approach is not possible, oil production is a derivative of geology to a lesser extent than technology. Industrial net production rates can be obtained from non-reservoirs in the usual sense. The system of technogenic fractures forms a reservoir associated with oil-saturated rock and the properties of such a system are described by too many parameters with high uncertainty and a number of assumptions [3–7]. On the other hand, there are forecasting methods based on solving the inverse problem of hydrodynamics. Having a sufficient amount of development data, it is possible to predict the dynamics of work based on statistical dependencies [9] or proxy material balance models.

The purpose of this work. The purpose of this work was to create a convenient methodology for calculating oil production from the reservoirs of the Bazhenov formation.

Methodology. The paper proposes and tests a method for predicting the dynamics of oil, liquid and gas production for wells in the Bazhenov formation based on a modification of the CRM dynamic material balance model (Capacity-Resistive Models — volume-resistive model).

Results. The method was tested when calculating the technological indicators of development for the object of one of the fields located in the KhMAO and showed its efficiency, which allows us to recommend it as a basis for drawing up project documents as an alternative to building a hydrodynamic model (GDM).

Background. The study performed by Rosneft employees shown in this paper demonstrates approach and analytical methods that allows to forecast oil production at the level of minimal infrastructure units. These approaches are used to forecast long-term oil production and predict infrastructure blockage. The approach was partially automated by the authors. This made it possible to testing at giant Krasnoleninskoye oilfield.

Aim. The study was performed in order to develop and test an approaches to forecast oil production of large oil fields with high detail levels.

Materials and methods. Common methods of decline curve analysis and water-into-oil curve analysis were used in this work to analyze the precondition. The main feature of the approach is the analysis of precondition at the level of large well clusters and transfer it to the level of wells. Some of the actions were automated by new proprietary software and were tested at the giant brown field. The software was integrated with the corporate database.

Results. An author’s approach has been developed. The approach allows to forecast oil production at the level of infrastructure units using analytical methods. Oil production of the giant brown field with high detail levels were forecasted using the proposed approaches and developed software.

Conclusions. The results show that the developed approaches and software can be used to forecast mediumand long-term performance of producing oil fields in the conditions of existing external and infrastructural constraints.

Background. The present work is devoted to one of the key areas of research activity of the modern oil and gas scientific world: decarbonization and increasing the efficiency of the natural and associated gas usage. One of the eco-friendly ways of processing natural and associated gas is the production of carbon black (soot) from it. This method is also included in the list of best available technology (BAT).

Nowadays, soot is a raw material for massive scale production of rubber products, which accounts for a large share of the manufacture of tyres, besides, carbon black is a valuable component in the paint-and-varnish and petrochemical industry (inks, plastics and many other things). The aim of the project is to assess the applicability of technologies for processing the surplus of associated petroleum gas (APG) into carbon black (CB).

Materials and methods. The technology is based on the pyrolysis of hydrocarbons under the influence of high temperature with a lack of air. In the work, the following tasks were performed: CB market was studied; the analysis and choice of the optimal method for obtaining soot from APG for the N field, technological calculation, and selection of equipment and economic evaluation of the technology were performed.

Results. Calculations have shown that the use of this method of APG utilization is cost-effective. The PI of the project is more than 2.

Conclusion. The main advantages of this technology are: relatively low capital outlays, efficient gas utilization, reduction of carbon dioxide emissions into the atmosphere, additional income from the sale of a new product in high demand. The main disadvantage of this method of gas utilization is the lack of experience and practice of oil companies in the possibilities and methods of carbon black from APG.

Background. Decision-making process in the oil and gas industry, traditionally extremely expensive, should be based on the point of maximizing the business value. Forecasting the effectiveness of investments of any business unit in oil and gas should be based on a data-driven management approach. The purpose of this article — to study methods and best practices of applying a data — driven approach to decision-making and analyze the possibility of scaling methods of best practices in the processes in oil and gas company.

Materials and methods. Research a various case with data-driven management shows that using data-driven approach allows solving several tasks at once:

to make a fast and quality decisions based on data that can always be checked, and the result can be analyzed;
to reduce the costs by eliminating inefficient steps and increase the flexibility of the process;
to form the correct attitude to data (data culture) and prepare for the implementation of the technologies of Industry 4.0.
Analyze cases revealed two common and important things: engineering of business processes from the key performance indicators and the technological development.

Results. In article discusses the topic of applying a data-driven decision-making approach in oil and gas companies using several examples of Gazprom Neft. These examples shows that better effect from the using of data-driven management is achieved by consistently modeling business processes for achieving maximum values; highlighting and fixing key business performance indicators and creating a digital monitoring of these indicators, which allows you to the achievement of goals.

Conclusions. In the conclusion of the article there are recommendation about using data-driven management approach for various processes of an oil and gas company.