Vol 4, No 2 (2022)

The modern development of seismic fieldwork technologies and data processing and interpretation techniques make it possible to obtain high-quality material not only for studying structural features, but also for in-depth analysis of rock anisotropy: a detailed understanding of the spatial heterogeneity of lithological and petrophysical properties, predicting the nature of fluid saturation, analyzing finely disjunctive tectonics, fracture density and propagation geometry.

Quality control at all stages of the field seismic survey and processing is also important. It is necessary to understand that quality control consists in direct participation in the work process from the beginning of seismic survey planning to obtaining the final result and interpretation.

This article discusses the importance of full technical support for seismic exploration from planning and design of field work for solving the geological problems, to choosing the optimal processing graph, the results of which have a significant effect on structural and dynamic interpretation. On the example of the South Torgay Basin field, the detailed design of seismic surveys, the use of the latest fieldwork and data processing technologies made it possible to obtain more complete geological and geophysical information. As a result of the work carried out, in 2021, all wells drilled on the basis of the new seismic survey obtained an industrial inflow of hydrocarbons.

Neural networks and machine learning have long been used by almost everyone in their daily lives, perhaps not always consciously. When an algorithm of social networks identifies the faces of people in a photo or a voice assistant helps us search for some information, machine learning techniques underpin all of these activities.

In recent years neural networks are finding more and more applications in the fields of oil and gas exploration and production. This article aims to illustrate an example of the application of neural networks in the analysis of seismic data for an active oilfield by predicting 3D cube of petrophysical properties to further detail the geological model and search for additional hydrocarbon accumulations.

One of the key conditions for successful prediction of petrophysical properties using neural networks is a wide sample of well data for effective training of a non-linear operator. In our case, since it is a producing field, there were more than 100 wells available, which fully meets the requirements of the algorithm. Another important condition for application of this technique is having high-quality well ties for the used wells, this step of the workflow will also be described within the article.

A distinct feature of neural network analysis, in contrast to classical inversion, is that it does not use a seismic wavelet. The neural network automatically determines such an operator that best describes the correlation between several seismic traces in the wellbore area and the log curve. This feature reduces the analysis time and produces express results if the above mentioned conditions are met, which makes the neural network technique an effective tool for dynamic analysis of seismic data.

The evolution of oil and gas reservoirs development parameters prediction has received new opportunities due to the development of digital technologies and computing power. The idea and first experiments in the use of artificial neural networks for various kinds of applied problems as classification of workover actions, automatic interpretation of geophysical well logging and core analyses results can be considered as an important milestone for the oil industry. The application of machine learning for reservoir development parameters prediction is currently a pressing and unresolved issue. Disputes arising in attempts to industrialize this technology are associated with so-called “black box” – a situation when the constructed model cannot explain physical laws and it is almost impossible to track intermediate results in the process of calculating non-linear dependencies. Given the problems described above, the current best practice is to combine machine learning models and physically meaningful analytical models as described in this paper.

For the past decades, long-term water flooding processes have to water channeling in mature reservoirs, which is a severe problem in oilfields. The development of better plugging ability and cost-effective polymer microspheres is a key aspect for the control of excess water production. Research on polymer microspheres applicable in a heterogeneous reservoir to plug high permeable channels has been growing significantly as revealed by numerous published scientific papers. This review intends to discuss different types of the polymer microspheres and oil displacement efficiency. The related difficulties and future prospects of polymer microspheres are also covered. The review provides a basis to develop polymer microspheres for future applications in oilfields. It will assist the researchers to further develop polymer microspheres to improve the oil recovery from mature reservoirs under economic conditions to meet the requirements of future oilfields.

This paper a numerical simulation of methods of intensification of flow to the well by using various approaches. To simulate the hydrochloric acid impact, an approach was applied based on changing the well productivity factor, as well as an approach that consisted of using a chemical reaction in a hydrodynamic model. The criteria for qualitative forecasting of technological development indicators was the actual data on one of the wells of the analogous field of the object. As a result of calculations on the example of a actual field in the conditions of extended horizontal wells, increments of additional oil production were obtained with applying various approaches of modeling the process of hydrochloric acid treatment. It was revealed that in the conditions of extended horizontal wells, the use of negative values of skin factors increases oil production by several times in comparison with the approach of compositional modeling with the occurrence of chemical reactions. Sensitivity analysis to the volume and concentration of the injected acid was carried out by using specialized software. It was concluded that as a result of the chemical reaction in compositional modeling, the effect of hydrochloric acid treatment significantly depends on the composition of the rock, the reaction rate, the concentration and volume of the injected acid. Multi-stage hydraulic fracturing was modeled by using a planar fracture system tool and discrete fracture system model. A slight discrepancy between the results of calculating the hydrodynamic model between these methods for modeling multi-stage hydraulic fracturing was identified.