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In order to design high-strength downhole flow control valve, realize stratified oil and gas production and dynamic regulation, improve mining efficiency, reduce pollution and promote the development of intelligent completion technology, this paper selects processing materials suitable for electro-hydraulic composite intelligent completion flow control valve based on field working conditions and the working principle of hydraulic control flow control valve. The structure of key components such as valve body, slide sleeve and throttle valve sleeve is designed, and the mechanical properties of key components of flow control valve are modeled and simulated successively by numerical simulation method combined with the actual service conditions of flow control valve in the underground, and the service reliability of flow control valve is clarified. The results show that: Under the coupling conditions of pressure 50 MPa, load 650 KN and temperature 125 °C, the maximum stress value appears on the surface of the throttle valve sleeve is 980 MPa, and the maximum deformation of the parts is controlled within 0.202 mm, and the strength of all parts is lower than the yield strength of the material, fully meeting the requirements of the field working conditions. This tool is of great significance for improving oil field recovery and intelligent well completion development.

Recently, maximum reservoir contacting (MRC) wells have attracted more and more attention and have been gradually applied to CO2 WAG injections. During the use of MRC wells for CO2 WAG injections, intelligent completions are commonly considered to control CO2 breakthroughs. However, the design of the operational and intelligent completion parameters is a complicated process and there are no studies on the co-optimization of the operational and intelligent completion parameters for CO2 WAG processes. This study outlines an approach to enhance the oil recovery from CO2 WAG injection processes through the co-optimization of the operational and intelligent completion parameters of MRC wells in a carbonate reservoir. First, a simulation method is developed by using Petrel and Intersect. Then, a series of simulations are performed to prove the viability of intelligent completions and to investigate the effects of the timing and duration of the CO2 WAG injection, as well as the type, number, and placement of intelligent completion devices on the performance of a CO2 WAG injection by MRC wells. Finally, the imperialist competitive algorithm is used to co-optimize the operational and intelligent completion parameters for MRC wells. The results show that compared with the spiral inflow control device (SICD), autonomous inflow control device (AICD), labyrinth inflow control device (LICD), and annular interval control valve (AICV), the nozzle inflow control device (NICD) is the best type of intelligent completion device for MRC wells. There is an optimal installation timing, inflow area, and number of NICDs for a CO2 WAG injection by MRC wells. The NICDs need to be placed based on the permeability distribution. The oil recovery for the optimal case with the NICDs reached 46.43%, which is an increase of 3.8% over that of the base case with a conventional completion. In addition, compared with the non-uniformity coefficient of the base case (11.7), the non-uniformity coefficient of the optimal case with the NICDs decreased to 4.21. This is the first time that the co-optimization of the operational and intelligent completion parameters of a CO2 WAG injection has been reported, which adds more information about the practical applications of MRC wells in CO2 WAG injections for enhancing oil recovery in carbonate reservoirs.

Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.

The relevance of assessing the applicability of intelligent wells using autonomous inflow control devices lies in the active development of the relevant sector of the oil and gas industry and the limited understanding of the economic efficiency of intelligent wells. The use of autonomous inflow control devices allows for a change in the composition of flow to the well, thus contributing to delaying the breakthrough of undesirable formation fluids, but at the same time, such an effect affects the dynamics of formation fluid production, which undoubtedly has a huge impact on the economic effect of the project. The analysis of scientific publications on the topic of “intelligent well completion” as a downhole production monitoring and remote production control system has shown that the vast majority of researchers pay attention to the evaluation of technological efficiency, ignoring the economic aspects of the proposed solutions. This study considered the dependence of the economic effect on the geological reservoir and technological well characteristics for variant 1—intelligent horizontal well (HW) completion using autonomous inflow control devices and variant 2—conventional horizontal well completion using the open hole. Calculations of production levels and dynamics in the two variants were performed on a created sector hydrodynamic model of a horizontal well operating in the depletion mode. The analysis of the obtained results allowed us to determine the applicability criteria of the proposed configuration of formation and well characteristics at the object of study, as well as to establish general dependencies of the net discounted income of an intelligent well. As a result of this study, it was determined that the economic efficiency of intelligent well completion with the use of autonomous inflow control devices relative to conventional well completion increases with decreasing permeability and drawdown pressure on the reservoir and reaches maximum values at the object of study at the thickness of the oil-saturated part of the reservoir about 5–6 m and the location of the wellbore in it at 35–40% of the thickness of the oil-saturated part below the gas–oil contact (GOC). This article covers the research gap in evaluating the economic efficiency of intelligent HW completion using AICD relative to conventional HW completion in oil rims.

There is a strong global demand for oil and gas resources, and forecasts indicate robust growth in oil demand in the coming years. Meeting this demand necessitates the exploitation of unconventional resources and enhancing the recovery of existing oil and gas fields. Field trials indicate that traditional gas injection in shale wells has low sweeping efficiency. Emerging technologies play an exceedingly significant role in solving the challenges of gas EOR in shale and tight formations. Among these advancements, smart or intelligent well technology has emerged as a promising solution to enhance field development outcomes. This study focuses on improving gas flooding efficiency in the Bakken formation by utilizing smart completions to reduce the gas–oil ratio (GOR) and increase oil recovery. An economic assessment of gas re-injection is conducted, considering both gas storage and enhanced oil recovery, with analysis incorporating capital expenditures, operating costs, and revenue from increased production. Reservoir simulations were employed to determine the most effective gas injection scenarios for maximizing recovery and storage efficiency. Simulation results demonstrate that 20% of perforated laterals account for 80% of the injected gas. To address this challenge, this work proposes using smart completions to segregate lateral sections, thereby optimizing gas injection efficiency, and unlocking additional oil in tight formations. Segregating horizontal laterals for gas re-injection using smart completion technology can achieve gas injection efficiencies of up to 0.25 barrels per Mcf of gas injected, with lower incremental gas production. The optimal injection rate is between 1 MMcfd and 3 MMcfd, with an injection period ranging from one to three years. It was also found that injecting gas into the toe section results in high bottom hole pressure but lower oil recovery due to reduced gas injection efficiency. From an economic perspective, the project yielded favorable outcomes, with a positive net present value (NPV) at a 7% discount rate. Even at lower oil prices (USD 50 per barrel), the Internal Rate of Return (IRR) was calculated to be 170%, indicating strong potential profitability.

This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.

This paper addresses the issue of offering a consistent 3D visual rendering of subsurface objects when databases face non-completion. Digital modelling of subsurface objects, like utility lines, underground buildings or tree roots, is a difficult task. Data available are incomplete and not precise. The in situ acquisition of existing objects to increase data quality is complex and, therefore, costly. In this paper, a methodology to obtain missing spatial and geometrical data through field or empirical means is proposed. In addition, confidence levels are assigned to existing and derived spatial and geometrical attributes. They are consolidated on a class level and visualized through a bounding shape, called secondary object.

In the process of oil reservoir waterflooding, natural water dump flood technology for reservoir pressure decline prevention is considered as an unconventional but technically less demanding, more economical and safer method in comparison to surface power water injection. With natural dump flood technology, a single well serves as a water producer from a water bearing layer (aquifer) and simultaneously through gravity and the pressure difference between the aquifer and the depleted oil reservoir, it serves as a water injector inside the oil reservoir without expensive and complex injecting water treatment facilities at the surface. With the use of such technology and the running of intelligent well completion, it allows for the permanent monitoring of water production, injection rates and temperature inside the chosen reservoir. In addition, in offshore operations, the use of a subsea wellhead with a mud line suspension system allows for the placing of the injector well at the best predetermined position for water injection in a targeting reservoir and, together with an efficient subsurface acoustic data acquisition system, leads to better reservoir management and well integrity improvement. The overview and critical reflection of the drilling and intelligent completion of a natural dump flooding well for reservoir pressure support in partially depleted oil reservoirs in the Persian/Arabian Gulf has been given, referring to both their preparation and execution phase. The possibility of applying natural water dump flood was also considered in the Croatian onshore Beničanci oil field through a pilot project of water injection into the Be-62 well.

U postupku zavodnjavanja ležišta nafte radi podržavanja tlaka prirodno zavodnjavanje ležišta smatra se nekonvencionalnim, ali zato tehnički manje zahtjevnim, ekonomičnijim i sigurnijim u usporedbi s metodom utiskivanja vode s površine. Tehnologija prirodnoga zavodnjavanja podrazumijeva upotrebu iste bušotine za proizvodnju vode iz vodonosnoga sloja te simultano, s pomoću djelovanja gravitacije i razlike slojnih tlakova u vodonosnome sloju i djelomično iscrpljenome naftnom ležištu, utiskivanje vode u naftno ležište bez izgradnje skupih i kompleksnih površinskih sustava za utiskivanje. Primjenom najsuvremenije tehnologije i vođenjem tzv. inteligentnoga opremanja bušotine omogućeno je stalno praćenje proizvodnje vode, kapaciteta utiskivanja i temperature unutar odabranoga ležišta. Osim toga, u odobalnim operacijama korištenje podvodne bušotinske glave sa sustavom za privremeno napuštanje bušotine omogućava najbolji položaj za utiskivanje vode u ležište i zajedno sa sustavom za prikupljanje podataka vodi do boljega upravljanja ležištem i poboljšanja integriteta bušotine na odabranome naftnom polju. U ovome radu dan je pregled i kritički osvrt na bušenje i inteligentno opremanje bušotina prirodnoga zavodnjavanja ležišta u djelomično iscrpljenim ležištima nafte u Perzijskome zaljevu, a odnosi se i na fazu pripreme bušotine i na samo izvođenje. Dan je i primjer mogućega korištenja tehnologije prirodnoga zavodnjavanja ležišta na hrvatskome (kopnenom) naftnom polju Beničanci koji je u visokome stupnju iscrpljenosti.

The latest intelligent fields include intelligent well completions capable of collecting downhole data, which allow the operator to selectively control completion intervals throughout the life of a reservoir. Performance can be monitored and optimized in real time and operations can be adjusted remotely using downhole equipment. To quantify and develop this potential, we deigned an algorithm-based system which is capable of optimizing intelligent well control to decide on whether or not to utilize intelligent well technology. Simulated annealing algorithm was used for obtaining an optimum control strategy and determining an operation that maximizes the net present value (NPV). In this article, we used more than 4,000 simulation runs which were performed automatically in a reservoir simulator to optimize a cyclic production scenario with intelligent well completions. The intelligent wells were equipped with on/off inflow control valves in each zone, which were opened and closed sequentially to maximize the oil rate while not exceeding limits for water production. The results show that the application of simulated annealing algorithm for optimization of intelligent well technology culminates in an increase in the NPV through 14 % increase in cumulative oil production and a significant reduction in water production.