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In order to improve the effect of injection–production coupling development to improve crude oil recovery in complex fault-block reservoirs, we carried out a physical simulation experiment based on a sandpack model of transforming water-driven development into injection–production coupling development and quantitatively evaluated the influence of rounds of injection pressure coupling on the crude oil mobilization in reservoirs with different permeability levels and on oil recovery. Meanwhile, the characteristics of residual oil were studied via a numerical simulation method. The mechanism of increased oil production via injection–production coupling development was revealed by analyzing the water and oil contents, formation pressure, and streamline fields through the establishment of mechanism models. The results of the physical experiment show that injection–production coupling can improve the recovery effect of medium- and low-permeability reservoirs by 55.66%. With an increase in the injection pressure, the oil recovery percentage of the low-permeability sandpack model at 20 MPa is 100%, and this study finds that injection–production coupling is the main way to develop the recoverable oil in a low-permeability reservoir. The numerical simulation results show that among the four remaining oil distribution types (interwell-enriched, low-permeability zone-enriched, well network imperfection, and mismatch between injection and production), the interwell-enriched type of the remaining oil reserves accounts for the highest proportion (48.52%). The simulation results of the mechanism model show that water-driven development easily leads to streamline solidification, resulting in ineffective circulation of the injected water. Compared with conventional water-driven development, the pressure propagation range is significantly increased in injection–production coupling development. The reservoir streamline distribution is more continuous and uniform, and the flooding wave is wider in volume and range. This research provides a theoretical basis for the injection–production coupling technology policy in complex fault-block reservoirs. [ABSTRACT FROM AUTHOR]

With the increasing share of variable and limitedly predictable renewable energy in power systems worldwide, ensuring reserve capacity to maintain the balance of supply and demand becomes more important. On the other hand, the development of the virtual power plant model (VPP) allows renewable sources and energy storage to participate in reserve service. This paper addresses the optimal reserve bidding strategy problem of a VPP comprising of renewable energy resources (RESs), energy storage systems (ESSs), and several customers. The VPP participates in balance capacity (BC), day-ahead (DA), and intra-day (ID) markets. The scheduling problem is formulated as a two-stage chance-constrained optimization model taking the uncertainty of RESs production, load consumption, and probability of reserve activation into account. The response of VPP after its reserve capacity is called and generated is also considered to increase the operational flexibility of VPP. The proposed model is implemented on a test VPP system, and the effects of RESs sizing, ESSs sizing, and the probability of reserve activation are analyzed. Results indicate that the proposed model can perform well under real-world conditions. [ABSTRACT FROM AUTHOR]

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations. [ABSTRACT FROM AUTHOR]

With the depletion of traditional energy resources, the share of heavy-oil production has been increasing recently. According to some estimates, their reserves account for 80% of the world's oil resources. Costs for extraction of heavy oil and natural bitumen are 3–4 times higher than the costs of extracting light oil, which is due not only to higher density and viscosity indicators but also to insufficient development of equipment and technologies for the extraction, transportation, and processing of such oils. Currently, a single pipeline system is used to pump both light and heavy oil. Therefore, it is necessary to take into account the features of the heavy-oil pumping mode. This paper presents mathematical models of heavy-oil flow in oil-field pipelines. The rheological properties of several heavy-oil samples were determined by experiments. The dependencies obtained were used as input data for a simulation model using computational fluid dynamics (CFD) methods. The modeling condition investigates the range of shear rates up to 300 s−1. At the same time, results up to 30 s−1 are considered in the developed computational models. The methodology of the research is, thus, based on a CFD approach with experimental confirmation of the results obtained. The proposed rheological flow model for heavy oil reflects the dynamics of the internal structural transformation during petroleum transportation. The validity of the model is confirmed by a comparison between the theoretical and the obtained experimental results. The results of the conducted research can be considered during the selection of heavy-oil treatment techniques for its efficient transportation. [ABSTRACT FROM AUTHOR]