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Norway's Equinor has finished the transfer of its minority stakes in joint ventures (JVs) with Rosneft to the state-owned Russian major, as well as signing off on an agreement to exit it's 30% share of the Kharyaga production-sharing agreement (PSA) operated by another Russian state-owned oil producer, Zarubezhneftegaz. The agreements effectively end Equinor's (previously Statoil's) more than 30-year presence in Russia, releasing it "from all future commitments and obligations" to any of its former Russian partners, according to a press release issued by the company on 25 May. Equinor joined Shell and BP in announcing on 27 February their intent to sever business ties with Russia, 3 days after Russian troops had crossed the border into Ukraine. On 2 March, ExxonMobil followed suit and has since declared force majeure on its Sakhalin 1 project with Rosneft, India's ONGC, and Japan's SODECO. Equinor announced an impairment of $1.08 billion on its balance sheet as of 31 March 2022, a move that Equinor's President and CEO Anders Opedal had predicted in February when he noted that the company's departure from Russia would "impact the book value" of the $1.2 billion in noncurrent assets it held in Russia at yearend 2021 and "lead to impairments."
Abstract Globally, steam injection for heavy and high-viscous oil recovery is increasing, including carbonate reservoirs. Lack of full understanding such reservoir heating and limited information about production and injection rates of individual wells require to forecast steam injection not only deterministic and simple liquid displacement characteristic modeling types, but also the data-driven one, which covers the adaptive modeling. The implementation and validation of the adaptive system is presented in this paper by one of the world's largest carbonate reservoirs with heavy and high-viscous oil of the Usinsk field. Steam injection forecasting in such reservoirs is complicated by the unstable well interactions and relatively low additional oil production. In the adaptive geological model, vertical dimensions of cells are similar to gross thicknesses of stratigraphic layers. Geological parameters of cells with drilled wells do not necessarily match actual parameters of those wells since the cells include information of neighboring wells. During the adaptive hydrodynamic modeling, a reservoir pressure is reproduced by cumulative production and injection allocation among the 3D grid cells. Steam injection forecasting is firstly based on the liquid displacement characteristics, which are later modified considering well interactions. To estimate actual oil production of steamflooding using the reservoir adaptive geological and hydrodynamic models, dimensionless interaction coefficients of injection and production wells were first calculated. Then, fuzzy logic functions were created to evaluate the base oil production of reacting wells. For most of those wells, actual oil production was 25 – 30 % higher than the base case. Oil production of steamflooding for the next three-year period was carried out by modeling two options of the reservoir further development - with and without steam injection. Generally, forecasted oil production of the option with steam injection was about 5 % higher. The forecasting effectiveness of cyclic steam stimulations of production wells was done using the cross-section method, when the test sample was divided into two groups - the best and the worst, for which the average forecasted oil rates after the stimulations were respectively higher or lower than the average actual oil rate after the stimulations for the entire sample. The difference between the average actual oil rates after the stimulations of the best and the worst groups was 32 %, i.e. this is in how much the actual oil production could have increased if only the best group of the sample had been treated.
Abstract The current combination of increasingly complex wellbores and tightening budgets forces operators to do more with less and find new ways to expand the drilling envelop. Often this pushes the parameters to the limit in order to achieve faster penetration rates. Operating at the limit or beyond impacts equipment reliability and project cost. A thorough failure analysis of the root cause(s) of every incident can help identify and address areas that need improvement. Identifying a cause fosters improvement while it simultaneously pushes the boundaries so the profitability of mature assets can be maximized. Typical failure analysis attempts to determine the cause of a failure and establish corrective actions to prevent reoccurrence. In a large extended reach drilling project targeting a mature field, the approach to a single failure was expanded and projected in a proactive manner to anticipate the impact of current failure modes in future more challenging scenarios. This innovative method combines the classic failure analysis approach with a comparative approach designed to identify and classify each factor that contributed to the failure. This information is then compiled into a dynamic predictive risk matrix to improve the planning. This method, thanks to the contextualization of individual failures and the multi-facet comparative analysis, revealed a pattern between reliability trends and environmental challenges. The pattern was correlated with the increased drilling difficulty over the lifetime of the project, and suggested that the long-established practices had to be revised to overcome the new scenario. The analysis contributed to the delineation of a strong action plan that immediately revealed a consistent service quality improvement quarter on quarter and nearly a 50% decrease in failure rate. The enhanced reliability had a direct impact on the performance that registered a significant reduction of the drilling time, thus lowering the overall well construction cost. In today's economics where cost reduction, resource optimization and sustainability are at the top of the operator's priority list, failure analysis has become paramount to ensure continuous improvement. Effective analytic methods to identify and eliminate showstoppers are needed to minimize unplanned events and deliver within budget. By digging deep into the root cause of incidents, this new approach to failure analysis enabled an enhanced, broader and more effective quality improvement plan that tackled service quality from multiple angles. From refining bottomhole assembly (BHA) design and risk matrix to drafting field guidelines and roadmaps, this approach also provided extra guidance and risk awareness for future well planning improvement. This particularly applies to mature fields where wellbore complexity increases at the same time budgets decrease and it's necessary to improve operational excellence to assure profitability.
Abstract In many oil and gas provinces not only in Russia, but throughout the world, carbonate strata make up a significant portion of the sedimentary cover, and large accumulations of hydrocarbons are associated with them. However, the purposeful study of them as reservoirs for hydrocarbons in our country practically began only in the post-war years. In the special petrography laboratory carbonate rocks composing various stratigraphic complexes of almost all oil and gas provinces of the Soviet Union were studied, and in particular, Paleozoic carbonate strata of the Timan-Pechora province, Ural-Volga region, Belarus, Kazakhstan, ancient Riphean-Cambrian formations of Yakutia and relatively young strata of the Late Cretaceous of the northeastern Ciscaucasia. Carbonates are widespread sedimentary rocks. A very significant part of them was formed in the conditions of vast shallow-water marine epicontinental basins. A large number of works are devoted to the study of such deposits. However, issues related to the conditions of formation of carbonate sediments and their postsedimentary changes cannot be considered resolved, as well as the classification of the rocks themselves. The analyzed field is the Osvanyurskoye one. It was discovered in 2007. The field is located in the north-east of the European part of the Russian Federation, 2 km from Usinsk in the Komi Republic. The field is a part of the Timano-Pechora oil and gas province and it is a mature field (fig. 1). The objective was a 2.5m thick layer of the Serpukhov horizon.
Cherenkova, M. A. (RN-Shelf-Arctic LLC) | Myatchin, O. M. (RN-Shelf-Arctic LLC) | Kleshchina, L. N. (RN-Shelf-Arctic LLC) | Solomatina, E. A. (RN-Shelf-Arctic LLC) | Obmetko, V. V. (Rosneft Oil Company) | Reidik, Yu. V. (Rosneft Oil Company)
The article has been prepared by specialists from Rosneft Oil Company and the Reservoir and Petroleum Engineering Department of RN-Shelf-Arctic LLC, a subsidiary of Rosneft which carries out geological research and hydrocarbon exploration within the Rosneft’s license blocks upon the Arctic and Far Eastern shelves of the Russian Federation. The article provides an overview of structure and depositional environments of the Permian clastic deposits within the marine extension of the Kolvinsky megaswell. There are large oil fields (Vozeiskoye, Kharyaginskoye, Yuzhno-Khylchuyuskoye) within onshore part of this vast area of oil and gas accumulation. These fields have wide stratigraphic range of oil-bearing capacity from the Lower Devonian to the Triassic. The importanсе of this research is associated with the proven petroleum potential of the Permian deposits within Timan-Pechora petroleum basin. Generalization of the previous results and comprehension with new detailed 3D seismic interpretation allowed the authors to: 1) detail the geological structure and depositional environments of the Permian clastic deposits; 2) identify and map the most perspective deposits that can be considered as a potential hydrocarbon reservoir; 3) reduce geological uncertainties associated with the presence and quality of the Permian clastic reservoir. The article is based on the results of interpretation of 3D and 2D seismic data, geological and geophysical data of adjacent areas, as well as regional geology of the Pechora Sea.
For a group of fields in the Khoreyver depression, the experience of estimating trends in the petrophysical properties of low-thickness carbonate reservoirs of the Upper Famennian is shown. The research was carried out both on the comprehensive analysis of available geological and geophysical data (seismic, boreholes, cores), and on the experience of previous years. The works included a detailed correlation of well sections, identification of lithofacies according to well logging and core data, creating of maps for this lithofacies. Then seismic attribute and neural analysis in both two- and three-dimensional modifications were performed. These parts allowed to get seismic facies classification and to calculate reservoir properties’ trends. This way gave an estimation map of lithofacies, as well as quantitative trend maps of effective thicknesses, NTG and porosity. Based on the results of the analysis of geological and geophysical information, the most perspective zones were identified for further additional drilling. The results of the research made it possible, despite the difficult geological conditions (small thickness, faults, diagenesis), to clarify the situation of sedimentation of the region; for the first time to obtain the lithofacies distribution, taking into account the seismic forecast, to get quantitative estimating maps for effective thickness and porosity. The results are currently taken into account as one of the factors when making decisions on the placement for new wells.
This month's SPE Spotlight on Young Professionals highlights George Buslaev, head of the drilling department at Ukhta State Technical University. Ukhta is an important industrial town in the Timan-Pechora Basin of Russia and has a long-standing history in the oil and gas industry. Being the son of a scientist-engineer who initiated directional drilling in the Timan-Pechora oil and gas province, Buslaev has always had a passion for the petroleum industry, receiving his PhD from the Ukhta State Technical University. Through his hard work and determination he has published 57 works, including 6 patents and 6 SPE papers. His engagement in a variety of fields made SPE become an integral part of his life.
The Yarega heavy oil field is an example of Russia's technical advancements in heavy oil and bitumen production. Describe the technologies deployed in the field and their effects on production. Due to the requirements for high oil production rates and final oil recovery factors, we applied a counterflow steam-assisted gravity drainage (SAGD) technology in Yarega, which resulted in even reservoir coverage by thermal exposure, higher oil production rates, and lower steam/oil ratio values. Modeling results also had shown higher expected final oil recovery factors. A surface/underground thermal oil mining field development method is in common use in the Yarega field.
Zakharova, O. A. (Gazpromneft NTC LLC) | Zagranovaskaya, D. E. (Gazpromneft NTC LLC) | Vilesov, A. P. (Gazpromneft NTC LLC) | Rasskazova, S. N. (Gazpromneft NTC LLC) | Stepanova, V. S. (Gazpromneft NTC LLC)
The article is devoted to the results of hydrocarbon prospects estimating on the Pechora sea shelf within the Gazprom Neft license areas. The area was clustered according to its geological and geophysical features. More than a hundred prospective objects are defined in six oil and gas complexes. Overall estimated hydrocarbon potential is more than 2,525 billion tons. The assessment is based on integration of regional 2D seismic data, new 3D seismic data from the three license areas, as well as core and outcrops data from adjacent areas. Based on sequence stratigraphic analysis, the reservoir properties and other parameters of the prospective objects were clarified. The promising objects are rated on the basis of key geological and geophysical indicators. Moreover the location for appraisal drilling for subsequent exploration is proposed. In addition to determining oil and gas content of Triassic, Permian and Carboniferous reservoirs, it is spoken in details about prospects of deep complexes. These complexes are reef traps of the Upper Franian, terrigenous reservoirs of the Middle Devonian and Prague stage, hypergene carbonate reservoirs of the Ovinparmian horizon (Lower Devonian) and Ordovician-Silurian carbonate rocks. Aiming to discovering giant fields on the Arctic shelf one of the technological challenges has been identified - drilling in difficult climatic conditions to prospective horizons at depths of more than 4.5-5.5 km. The proposed exploration and R&D program allows to determine the optimal well location and to solve non-standard technological challenges to achieve this ambitious goal.
Abstract This paper outlines the study results of machine learning application to automate well correlation. Algorithms are designed to address challenges such as geological field complexity, number of wells and geological horizons, and quality of log data, that are usually associated with well correlation The Kansas state oil and gas fields, USA has more than 350000 wells and produced over 5 billion barrels of oil, with an estimated 11 billion barrels of oil still remaining underground. Nearly 100 wells from Thomas, Scott, Logan, Barton, Stafford and Wichita Counties are selected for this study. Petrophysical data such as Gama Ray, Neutron, and Density logs, considered most sensitive for lithology and Geological Cores descriptions are used as input. Sedimentary facies variation from limestone, shale, sandstone and coal were used as indicators to separate stratigraphic sequences. The approach followed is a 1D adaptation of panoramic stitching using feature vectors generated from a 1D Convolution Autoencoder. Convolutional autoencoders learn a compressed representation of input by first compressing the input using encoder and decompressing it back using decoder to match the original output This autoencoder is a neural network trained to reproduce input image in output layer, similar to facial recognition techniques. It identifies a series of matching points between a pair of well logs and trained to pick and match geological well tops. In order to demonstrate the performance of algorithms in term of accuracy, speed and to address the range of uncertainties, more than 30 wells were used for training. The study results have demonstrated high level of constancy in automatically subdividing the reservoir units of different wells consistent with stratigraphy and consistent across wells even though distance separating for each individual well is considerably more than one kilometer.