The aim of the work is the regional mapping of the Coniacian-Santonian sediments potential in the territory of the Noyabr’sky region. The study used all available geological and geophysical information on the region. Mapping of structural "bright spot" anomalies in seismic data was performed and the reservoir potential index was determined according to well log data. Based on the information obtained, the most promising areas identified and an optimal approach to the planning of pilot for both new and the old wells proposed.

Geological and economic assessment (GEA) of new exploration projects is based on an integrated analysis of all uncertainties and risks of its implementation. GEA includes several stages: evaluation the volume of geological resources and probability of success of deposit discovery; production potential of the deposit; design and evaluation of infrastructure solutions; the cost of discovery and development of oil field and finally economic effect of the project realization. Usually GEA-process takes a long time of teamwork and utilizes specialized software which incorporate probabilistic analysis methods. Development of the IT-solutions for complex geological and economic assessment will allow the company to significantly optimize this process. The first step on the way to full automation of GEA became the software for probabilistic geological evaluation Geomate:VEGA developed in Gazpromneft NTC LLC and launched into commercial exploitation in the group of companies Gazprom Neft in 2017.

This article considers peculiarities of reservoir discrimination in carbonate deposits of Baleikinskoe field based on a quantitative criterion. The article shows existing uncertainty in reservoir discrimination and its impact on volumetric parameters.

Тhe results of acoustic inversion as a tool for porosity forecasts of the target Early Vendian reservoir are considered in the article, depending on the presence of low-frequency energy 6-12 Hz in 3D land seismic data. The comparison of geological interpretation options of the obtained forecast maps is shown, the quantitative estimation of the forecast accuracy is given.

The article represents the results of a comprehensive analysis of the internal structure and conditions of formation’s influence on the reservoirs oil content. In the course of the analysis, the conditions of reservoir formation were reconstructed, regional and detailed facial schemes were constructed, reservoir properties were taken into account within each of the selected facies, hypsometric marks of reservoir were determined. The obtained data were compared with the oil reserves within the studied objects.

The express estimation method of additional study influence on the individual geological parameters uncertainty is considered. The geological features typification and study is suggested. The section geological features, such as vertical and horizontal anisotropy, are divided into typical 9 classes. In addition to the features parametric characteristics (porosity, saturation, effective thickness, fluid type and several others) are included discretely in the evaluation. For each set of geological parameters, different classes of knowledge are distinguished. Based on this discretization, the study effect on the distribution of reserves and their sensitivity to more or less known parameters was evaluated. The work idea lies in the allocation of typical objects with identical geological features. Further comparison of the study baseline state with the target gives an understanding of both the magnitude of the change in the ranges of stocks and the possibilities of minimizing uncertainties by specific measures based on the calculations already performed. The result of the assessment can be built into existing tools for portfolio analysis, resource flow plans, drilling rating or the development of integrated projects for project development.

Conventional approach to project exploration strategy planning at Identification/Access stage is usually focused on hydrocarbon presence confirmation and uncertainties decreasing. Further, at the end of the Appraisal stage, the main purpose is creating successful business case. However, focus on economic value of total project at Identification stage may lead to more optimal exploration program and increasing project EMV. The objective of this case study is to describe specific approach to establish major project exploration strategy at initial stage, based not only on uncertainty decreasing, but also on early business case development and maximizing future economic value. Case study project is framed within license area in the north-eastern part of the Pannonian Basin of Eastern Europe.

Method of selection of well-candidates and forecasting of water shutoff treatment efficiency in the context of Yamburgskoe oil, gas and condensate field (YOGCF) is introduced in this paper. The main purpose of this research is development of method of selection well-candidates and forecasting of water shutoff treatment efficiency based on the reservoir characterization and technological parameters while water shut-off works. Research tasks: Water shut-off works efficiency criteria selection and making its economic-mathematical model; The most significant factors of efficiency water-shutoff works detecting (based on mathematical model); Forming of series with wells for analysis; Mathematical model application for forecasting water shut-off treatment efficiency. Modern methods of data analysis (correlation analysis, regression multiple factor analysis) are used in this research. Analysis of water shut-off activities in YOGCF’s production gas wells in 2014 has been made. As a result, the most significant factors of water shut-off works efficiency have been selected. Researcher fitted mathematical model of water shut-off works efficiency assessment based on profitability criteria from incremental ultimate natural gas recovery. Obtained model is statistically significant on 95% confidence level (alpha = 0.05) and can be used in water shut-off works efficiency forecasting. Analysis of water shut-off works efficiency in production gas wells of YOGCF revealed probable economic effect from implementation of this method amounted to more than 150 million rubles per year (in case with 33 wet gas wells of Cenomanian gas deposit of YOGCF in 2014) because of more correct well-candidates selection.

In conditions of a high percentage of mechanization of the oil production process (98 % of the existing well found), the continuous process of oil production with the ESP directly depends on the quality and continuity of energy supply. Increasing the reliability of electrical equipment during thunderstorm activity and reducing the number of stops of submersible electric centrifugal pumps is the main direction for reducing oil production losses. Analysis of the shutdown statistics in combination with monitoring the automatic start of submersible pumps allowed to reduce oil losses by increasing the efficiency of operation of the Company's mechanized wells.

At present time, coiled tubing units perform almost all types of intervention operations. These units are fully automated, and in fact are pilot models of future drilling and workover rigs. Unfortunately, reserves structure, depth and accessibility are becoming more complex each year so that standard operations no longer address challenges we face today. Drilling of more complex wells require development and application of non-standard equipment. This includes multistage fracturing completion systems and CT as the main tool that meets modern requirements. Nowdays, there is a clear tendency for the increase in CT length and diameter. CT wall thickness has also been changed. Service companies start to apply tapered CT strings optimized for certain well conditions. Equipment, which is discussed in the article has not yet been used in the fields of Gazprom Neft. Therefore, it is very important to approach the problem of the correct selection of all key aspects both from a technical and technological point of view in advance.

The development of information systems allows you to collect, filter, store and process data of the field, describe the physical processes of oil and gas reservoirs and ground infrastructure. Enhanced capabilities of intelligent information systems allows a new angle to look at improving the efficiency of oil and gas field. We can greatly improve the operating efficiency of the asset and the quality of management decisions by combining the rapid collection of data on all systems field with integrated asset model and provide a computational libraries, allows the analysis of the technological system and provide skilled suggestions for optimization and potential loss in future (proactive protection). An integrated approach will allow for the optimization of the global instead of local, which is currently carried out by each service independently. It allows all professionals to focus their efforts on achieving a common goal.

In the world practice the increasing attention is paid to PVT simulations. The share of hydrocarbon fields, the fluids of which are characterized by complex behavior when changing geological and physical conditions, increases. Number of publications, devoted to PVT-modeling, grows with every year, the complexity of models increases. On the one hand, this helps to increase the accuracy of the forecast and to adopt an optisolution in the design and development of deposits, on the other - often slows the process of preparation of PVT of data

Cognitive technologies could be the key to rapid improvement of knowledge management effectiveness when applied in oil & gas industry. Classic knowledge management approach requires high level of employee engagement in knowledge management processes, as employees need to have a will to manually discover, capture, classify, share and apply knowledge. Cognitive technologies may lower that requirement as they automate processes of knowledge discovery, capture, classification and sharing; they also improve knowledge applicability by proposing right knowledge taking into account employee context. Authors propose a way in which combination of machine learning, data mining, text analysis and cognitive search could propose a new stage in knowledge management discipline.