The Pashyian Regional stage (horizon) is the main productive unit of the middle Devonian clastic succession of the South Tatar arch. This article presents, for the first time, maps of the lower and upper parts of the Pashyian, including data on sand-shale ratio, number of sand layers (reservoirs) and thickness, based on the analysis of logging data from 25,000 wells. The maps were created by spatial interpolation of Natural Neighbor and ArcGIS Pro software. The model of sedimentation of the Pashyian Regional stage reflects the interpretation of the plotted maps as well as the synthesis of the results of detailed core investigations (lithological, sedimentological, ichnotextural, petrophysical, etc.) and analysis of archive and published materials. The main points of the proposed model are as follows. The Pashyian sediments were formed in a marine basin, in an environment comparable to that of the middle shelf of modern seas – in an offshore zone dominated by current activity. The basin floor was a relatively flat plateau, on which sandy, silty and clay sediments were simultaneously accumulated. Sediments of all types accumulated during sea transgression. Sea regression caused erosion and destruction of the already formed sediments. Positive landforms of seabed relief, composed predominantly of sandy well-sorted material, comprised autochthonous underwater sand bars, formed by constant currents parallel to the bathymetric contour of the seabed. Underwater sand bars formed extensive systems nearly throughout the entire territory of the modern South Tatar arch. At the same time, allochthonous, poorly sorted, less mature sediments were formed in underwater troughs produced by transversal currents (directed from the shore towards the sea). The proposed model explains the consistent thickness of the Pashyian Regional stage, the mosaic distribution of sand bodies over the area, and the lens-like shape of the sand and siltstone reservoirs. The model can be extrapolated to other stratigraphic intervals of the Devonian clastic succession with similar sedimentological features.
(Kazan federal university), A.R. Ismagilov (Kazan federal university), D.S. Voloskov (Kazan federal university), M.S. Magdeev (PJSC "Tatneft"), A.A. Nazarov (PJSC "Tatneft") SUMMARYThe increased interest for geological and reservoir simulation model construction for the old fields raises the issue of reinterpretation of well logging data of the old well stock, taking into account the concepts of geology, specified during the period of field production. This work shows the results of mathematical approach development for automatic interpretation of well logging data of the old well stock. The work is aimed to solve the problem of fast reinterpretation of a standard logging set using machine learning algorithms. The solutions obtained for the determination of stratigraphic boundaries with the use of logistic regression and the detection of lithotypes basing on the support vector machine are presented. Mathematical algorithms and approaches to use them are presented and described.
With the development of unconventional resources the standard task of evaluation the porosity and oil saturation needs new approach. Heavy oil and bitumen can act as cement in many sand reservoirs, which excludes the use of standard measuring techniques. A new method for estimating the porosity and oil saturation of heavy oil reservoirs, based on wavelet X-ray computed tomography histogram analysis, was proposed. Its advantages are the ability to work with loose sand reservoirs, for which it is not possible to use standard assessment methods. A comparative analysis of the proposed method with a standard method for estimating porosity based on sample saturation with kerosene showed that the values of the porosity coefficient in the tomography resolution range 5-15 μm differed by 3-7% in the direction of the wavelet analysis method. This effect is due to the presence of transient voxels – mixels.
In the conditions of the dynamically changing conjuncture of the oil and gas market, there is an urgent need to reduce the cost of oil production and increase the efficiency of development, this is especially important for the local ultra-viscous oil. In this regard, it is necessary to optimize costs at all stages, starting from the geological exploration and even at the stage of completion of the development process. For ultra-viscous oil deposits, this is especially relevant at the stage of assessing the resource potential of a separate uplift of any of the fields, when the only reliable way to perform a high-frequency section at shallow depths is to drill appraisal wells with full core sampling. An additional load is exerted by the period between core extraction and obtaining information about the flow properties of each of the samples. By themselves, standard core studies are complicated by the fact that sand rocks of weakly cemented bitumoids can often be destroyed during experiments. In this regard, the use of new approaches, including digital ones, which allow us to make quick decisions on a part of the geological section in the area of the appraisal well and on the uplift as a whole, are highly in demand. This article describes the methods that allow the determining of flow properties for uncemented (loose sands) rocks in Permian sediments. More than 25,000 core samples were studied from 805 wells at several fields of the Republic of Tatarstan. The technology used allows us to calculate a continuous curve of volumetric bitumen saturation in the conditions of complete or partial absence of core at the well. This paper presents the results of creating an algorithm for automatic prediction of weight bitumen saturation in a sand pack of the Sheshminsky horizon of the Permian system using neural network technologies, as well as using an alternative calculation method.
A significant part of the hydrocarbon reserves in the Republic of Tatarstan belongs to heavy ultra-viscous oil. At the moment, due to the oil price rise, the development of these deposits is an actual task. In the recent decades, development planning has traditionally included the creation of three-dimensional reservoir models. The approaches that are also used are traditional and include data quality control, well log interpretation (determination of stratigraphy and calculation of reservoir properties), construction of a three-dimensional grid and filling it with properties. Meanwhile, the active development of information technology and artificial intelligence makes it possible to automate some of the routine processes. The purpose of this work is to create a chain of software algorithms combined under a digital platform for automating the process of constructing a geological model of ultra-viscous oil (hereinafter, UVO) deposits and calculating reserves on the example of the Republic of Tatarstan. The paper presents the general approaches that made it possible to solve part of the routine tasks of a geologist when constructing UVO deposit models. The tasks to be solved included the automation of stratigraphic boundaries definition, core-log matching, calculation of reservoir properties for wells, as well as determination of OWC position and placement of additional wells taking into account surface constraints. The approaches presented in this work are developed on the example of the UVO deposits of the Republic of Tatarstan, however, the principles used can be transferred to similar objects with the modification of the features used.
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