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SummaryEnhanced oil recovery (EOR) in fractured carbonate reservoirs is challenging because of the heterogeneous and oil-wet nature. In this work, a new application of using polymer nanospheres (PNSs) and diluted microemulsion (DME) is presented to plug fractures and enhance water imbibition to recover oil from the tight, naturally fractured carbonate reservoirs. DME with different electric charges is compared through contact-angle and core-imbibition tests to evaluate their performances on EOR. The cationic DME is chosen because it has the fastest wettability-alteration rate and thus the highest oil recovery rate. Migration and plugging efficiency tests are conducted to identify the screened particle sizes of PNSs for the target reservoir cores. PNSs with a particle size of 300 nm are demonstrated to have the best performance of in-depth propagation before swelling and plugging after swelling within the naturally fractured cores are used in this study. Then coreflooding experiments are conducted to evaluate the EOR performance when PNSs and DME are used together, and results indicate that the oil recovery rate is increased by 24.3 and 44.1% compared to using PNSs or DME alone. In the end, a microfluidic experiment is carried out to reveal how DME works with PNSs.

Intermittent pumping is an effective measure to reduce energy consumption and improve production time rate for tight oil wells with extremely low oil production. However, the intermittent pumping scheme is mainly controlled by manual timing or RTU remote timing in the cloud. The design of intermittent pumping interval lacks reasonable theoretical basis and the control efficiency is relatively low. Therefore, an integrated intelligent intermittent pumping device combining edge optimization analytic and edge control is developed. A closed-loop integrated device containing real-time data acquisition and transmitting module, optimization module and edge control module is realized. The optimization module embeds the pump fullness calculation model and intermittent pumping scheme optimization model. Instead of using dynamometer cards, a deep learning model of transferring power curves to dynamometer cards named PTD (Power to Dynamometer) is established through deep learning in order to lower the oilfield IoT cost. The average single well area error of predicted dynamometer card is less than 3%. To transfer the PTD to untrained new wells, a parallel model is established by parallel connection of PTD and transferring learning model. The parallel model is embedded into the edge computing device to realize the dynamometer card real-time prediction and pump fullness calculation. The calculated data is transmitted to scheme optimization model where a reasonable intermittent pumping scheme can be optimized. Then the optimized pumping scheme is transmitted back to the edge control device to adjust the pumping scheme to realize loop control. This technology was applied to 100 wells in the oilfield, the monthly energy consumption reduced by 30%, and the average pump efficiency increased by about 17%. The application of this technology is insightful to promote the IoT and intelligence of oilfield artificial lifting system.

Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy, and have data delay. An online multiphase flow detection method is proposed based on magnetic resonance technology, and its supporting device has been made and tested in lab and field. The detection technology works in two parts: measure phase holdup in static state and measure flow rate in flowing state. Oil-water ratio is first measured and then gas holdup. The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid. A highly compact magnetic resonance spectrometer system and intelligent software are developed. Lab experiments and field application show that the online detection system has the following merits: it can measure flow rate and phase holdup only based on magnetic resonance technology; it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time; it can detect oil, gas and water in a full range at high precision, the detection isn't affected by salinity and emulsification. It is a green, safe and energy-saving system.

Tight oil reservoir development is faced with the key technical problem that “water cannot be injected and oil cannot be produced” yet. With the diphenyl ethers water-soluble (gemini) surfactants as water phase shell and C10–C14 straight-chain hydrocarbon compounds as oil phase kernel, a nanofluids permeation flooding system was prepared by microemulsion technology, and its characteristics and EOR mechanisms were evaluated through experiments. The system has the following five characteristics: (1) “Small-size liquid”: the average particle size of the system is less than 30 nm, which can greatly reduce the starting pressure gradient of water injection, and effectively enter and expand the sweep volume of micro-nano matrix; (2) “Small-size oil” : the system can break the crude oil into “small-size oil” under the flow condition, which can greatly improve the percolation ability and displacement efficiency of the crude oil in the micro-nano matrix; (3) Dual-phase wetting: the system has contact angles with the water-wet and oil-wet interfaces of (46±1)° and (68±1)° respectively, and makes it possible for capillarity to work fully under complex wetting conditions of the reservoir; (4) High surface activity: the interfacial tension between the system and crude oil from a tight oil reservoir in Xinjiang is 10−3–10−2 mN/m, indicating the system can effectively improve the displacement efficiency of oil in fine pore throats; (5) Demulsification and viscosity reduction: the system has a demulsification and viscosity reduction rate of more than 80% to inversely emulsified crude oil from a tight oil reservoir in Xinjiang, so it can improve the mobility of crude oil in the reservoir and wellbore. The system can be used to increase oil production by fracturing in tight reservoirs, replenish formation energy by reducing injection pressure and increasing injection rate, and enhance oil recovery by displacement and cyclic injection, providing key technical support for effective production and efficient development and recovery enhancement of tight reservoirs.

CO2-responsive smart fluids have been widely investigated in the past decade. In this article, we reported a CO2-responsive smart fluid based on supramolecular assembly structures varying from vesicles to wormlike micelles.

The carbonate reservoir S is a giant limestone reservoir in H Oilfield, Iraq. Although the reserves account for 25%, the production contribution is only 0.4% to the total oilfield production due to poor petrophysical properties. Accordingly, the first proppant fracturing on vertical well was successfully executed in December 2016, which has already achieved a steady production period over than 3 years. In order to further improve the productivity, the first multi-stage proppant fracturing(MSPF) on horizontal well(SH01X) was successfully applied in November 2019, a technique which is rarely reported for porous limestone reservoir in the Middle East. Proppant fracturing in carbonate reservoirs is a technique difficulty worldwide, especially this is a lack of experiences in the Middle East. To ensure the success of this campaign, a holistic technical study including geology evaluation, reservoir performance analysis, drilling trajectory design, completion and fracturing technique design have been carried out based on principle of "geology-engineering integration". This paper will present a comprehensive illustration including treatment design (main completion-fracturing technique, total scale, fracturing fluid, proppant), job execution (mini-frac, main-frac) and post-frac production performance for this successful campaign. True vertical depth (TVD) of Well SH01X is 2720 m and the horizontal section length is 811 m. Based on the main technique of multi-stage proppant fracturing with open hole packers and sliding sleeves, totally 3784.3 m3 fracturing fluid and 452 m3 proppant were pumped in 8 stages. The test production was 3214 BOPD (choke size: 40/64", wellhead pressure: 970 psi). A historical breakthrough in the productivity of S reservoir has been achieved by the campaign. The post-frac evaluation shows that the treatment parameters are consistent with the design. The connectivity between artificial fractures and formation is greatly improved, and the stimulation effect is significant. Currently the "production under controlled pressure" mode has been executed and the stable production under stimulation target rate has been maintained. The systematic "geology-engineering integration" workflow is of significance to the success of the treatment as well as the stimulation effect. MSPF is planned to be a game-changing technique to develop the huge reserves of S reservoir. The experience gained from this case could provide theoretical as well as practical references for similar reservoirs in the Middle East.

The conventional configurations of pumping well IOT consist of electric parameter indicator and dynamometer. The current, voltage, power, and other electrical parameters are easy to access, low costs, stable, and acquired daily during pumping well operation. If the working condition diagnosis and virtual production metering of pumping well can be realized through electrical parameters, the utilization of dynamometers can be cancelled or reduced, which is of great significance to reduce the investment and improve the coverage of IOT in oil wells. The conventional methods of diagnosis and analysis based on electrical parameters and virtual production metering are lack of theoretical basis. The combination of deep learning technology of big data and traditional methods will provide solutions to solve related technical problems. Considering that there are many energy transmission segments from the motor to the downhole pump, the characteristics of the electric parameter curve are more sophisticated and difficult to identify compared with dynamometer card due to the influence of the unbalance, pump fullness, rod/tube vibration, wax deposition and leakage. The shape characteristics of the electric parameter curve of the pumping well are analyzed in the time domain and frequency domain, which provides the basis for further diagnosis, analysis and production measurement. In this paper, an integrated multi-model diagnosis method is proposed. For the working conditions with a large scale of samples, the electrical parameters are converted to dynamometer cards for diagnosis by using the deep learning technology of big data. For the working conditions with sparse samples, the machine learning model is used to diagnosis directly with electrical parameters. The deep learning electric parameter model for production measurement is established. Through the combination of the big data model of electric parameters to dynamometer card, 3D mechanical model of rod string, and big data model of plunger leakage coefficient, the virtual production metering function of pumping well based on electrical parameters is successfully realized. The diagnosis and virtual production metering method and software based on electrical parameters have been applied in many oilfields of CNPC. The accuracy of identifying the upper and lower dead points of electric parameters is 98.0%; the coincidence rate of working condition diagnosis under electrical parameters is 92.0%; the average error of virtual production metering with electric parameters is 13.4%. The dynamometer and gauging room have been canceled in the demonstration area. The application of electrical parameters to diagnose working conditions and meter the production of pumping wells is the key to the low-cost IOT construction. Traditional mathematical and physical methods are difficult to solve this problem, but the application of big data analysis technology could do the job successfully.

Cardiac fibrosis is a hallmark of various heart diseases and ultimately leads to heart failure. Although long noncoding RNA (lncRNA) SNHG20 has been reported to play important roles in various cancers, its function in cardiac fibrosis remains unclear. The expression of SNHG20 and microRNA 335 (miR-335) in heart tissues of angiotensin II-induced mice and angiotensin II-stimulated mouse cardiomyocyte cell line HL-1 were detected by quantitative real-time PCR (qRT-PCR). Cell viability was evaluated by cell counting kit-8 assay. The expression of galectin-3, fibrosis-related proteins (fibronectin, collagen IaI, and α-SMA), and apoptosis-related proteins [cleaved caspase-3 and cleaved poly(ADP-ribose) polymerase (PARP)] was detected by Western blotting. Bioinformatics prediction, luciferase reporter assay, and RNA pulldown assay were performed to determine the relationship between SNHG20 and miR-335 as well as miR-335 and Galectin-3. Gain- and loss-function assays were performed to determine the role of SNHG20/miR-335/Galectin-3 in cardiac fibrosis. SNHG20 was significantly upregulated and miR-335 was downregulated in heart tissues of angiotensin II-treated mice and angiotensin II-stimulated HL-1 cells. Downregulation of SNHG20 effectively enhanced cell viability and decreased cell size of HL-1 cells and the expression levels of fibrosis-related proteins (fibronectin, collagen IaI, and α-SMA) and apoptosis-related proteins (cleaved caspase-3 and cleaved PARP), which were induced by angiotensin II treatment. Furthermore, SNHG20 elevated the expression levels of Galectin-3 by directly regulating miR-335. Our study revealed that downregulation of SNHG20 improved angiotensin II-induced cardiac fibrosis by targeting the miR-335/Galectin-3 axis, suggesting that SNHG20 is a therapeutic target for cardiac fibrosis and hypertrophy.

The conventional foaming agents have the problems of poor adaptability and high cost during the application in different types of gas fields, especially in high temperature, high salinity, high acidic gas and high condensate oil and gas fields. In this study, the Gemini foaming agent was used as the main agent to enhance foaming and foam stability of the foaming agent, the grafted nanoparticles were used as foam stabilizer to further improve the foam stability, and the characteristic auxiliaries were added to make the foaming agent suitable for different types of gas reservoirs. Two types and six subtypes of nanoparticle foaming agents have been prepared for the main gas fields of China. The experimental evaluation results show that the overall temperature resistance, salinity resistance, H2S resistance, CO2 resistance and condensate resistance of the nanoparticle foaming agents can reach 160°C, 250 000 mg/L, 100 mg/L, 100% and 40%, respectively. The new foaming agents have been used in 8685 wells in China. Compared with conventional foaming agent, the average gas flow rate per well increased by 62.48%, the pressure difference (casing-tubing) decreased by 18.9%, and the cost dropped by 45%. The effect of reducing cost and increasing efficiency is obvious. Key words: water-yielding gas well, Gemini surfactant, nano-particle, characteristic auxiliaries, foaming agents series, drainage gas recovery

In the petroleum industry, detection of multi-phase fluid flow is very important in both surface and down-hole measurements. Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus. NMR is an efficient and accurate technique for the detection of fluids; it is widely used in the determination of fluid compositions and properties. This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance (NMR) detection. The online NMR data acquisition, processing and interpretation methods are proposed to fill the blank of traditional methods. A full-bore straight tube design without pressure drop, a Halbach magnet structure design with zero magnetic leakage outside the probe, a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation. Through the innovation of this work, the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.

A zwitterionic surfactant, hexadecyl hydroxypropyl sulfo betaine, was examined for its ability to stabilize supercritical CO2 (SC-CO2) foam, with the goal of improving the oil recovery from mature ...

Purpose This paper aims to explore the influence of CO2 partial pressure, flow rate and water cut on N80 steel corrosion behaviors in the displacement process of oil in glutenite reservoir by CO2 injection. Design/methodology/approach A self-made 3 L high-temperature and high-pressure autoclave was used to conduct corrosion simulation experiments of N80 steel in different CO2 partial pressures, flow rates and water cut (the independently developed oil and water mixing approach can ensure the uniform mixing of oil and water in experiments). Techniques like weight loss and surface analysis were used to analyze the corrosion behaviors of N80 steel under different conditions. Findings Results showed that the average corrosion rate of N80 steel accelerated at varying degrees with the increase of CO2 partial pressure, flow rate and water cut. Excluding that the samples showed uniform corrosion under the two conditions of 0.5MPa CO2 partial pressure and static corrosion, they displayed mesa attack corrosion under other conditions. Besides, with the increase of CO2 partial pressure, the pH value of solution dropped and the matrix corrosion speed rose, hence leading to the increased Fe2+ and CO32− concentration. Meanwhile, a lowered pH value improved the FeCO3 critical supersaturation, thereby leading to an increased nucleation rate/growth rate and ultimately causing the decrease of the dimension of FeCO3 crystallites formed on the surface of the samples. Originality/value The results can be helpful in targeted anti-corrosion measures for CO2/oil/water corrosive environment.

Purpose The purpose of this paper is to solve the tubing corrosion problem of B Block on the Right Bank of Amu Darya river sour gas field. Design/methodology/approach By using four-point-bending method, the tubing’s ability to resist sulfide-stress cracking was tested. Simulating the wellbore corrosive environment, the corrosion inhibitor which was suitable for gas filed had been screened. According to the characteristic of Amu Darya river gas field, the corrosion monitor system had been designed. Findings From the feedback of wellbore corrosion monitor result, the corrosion rate was lower than 0.076 mm/a. Originality/value This anti-corrosion technique provides security for the development of gas field.

Sucker-rod pumping wells are the most widely used producing wells in China. 94% of the 200,000 oil wells in CNPC are sucker-rod pumping wells. It is urgent to reduce the cost of every single well based on the well diagnosis and optimization methods under the background of low oil price and the IoT. Rich working experience of field engineers could help them diagnose some conspicuous abnormal well conditions by electrical power curves easily, but the scientific diagnosis methods have still not be established, and the potential of electrical power curves of the producing well is far from being fully tapped. The aim of this work is to diagnose the working condition of the sucker-rod pumping wells both on and under the ground based on the data from electrical power curves by machine learning. The methods shaped by the learning of the electrical power curves from nearly 600 wells mainly separate into 3 steps. The first is the diagnosis of conspicuous abnormal well conditions such as motor belt burning, motor belt slippage, two phase electrics, upper rod break, lower rod break et al. The prediction experience was obtained from the statistical learning of the mean and variance values after we equally split the 600 power curve values into 10 sub-groups. The second is the diagnosis of complex abnormal well conditions such as abnormal mechanical sound, slight tube leak, severe tube leak, pump stuck et al based on the combination of statistics and template vs diagnosed sample analysis. The third is the diagnosis of pumping conditions characterized by the remarkable prediction ability via deep learning. A surface well condition database was established and the corresponding electrical power curves were marked in real time. Based on the CNN technology, the model could recognize different pump working conditions such as insufficient liquid, gas influence, traveling valve leak, standing valve leak et al very well. The work has been applied in the oil fields of Jilin and Daqing. The method has been tested on nearly thousand hundreds of producing well utilizing sucker rod pumping system. The model demonstrates very high accuracy with almost 90% similarity to the result diagnosed by corresponding pump dynamometers for large sample and 94% of abnormal well working conditions for small sample. What’s more, the work would reduce millions of investment on the sensors, equipment and manpower for the management of producing wells in CNPC each year in the context of industrial IoT.

With the depletion of reservoirs, it is inevitable that once very prolific conventional and unconventional wells become stripper wells characterized as producing no more than 35 barrels of liquid equivalent per day over a 12-month period. The conventional metering strategy is not economical for its huge investment in facilities, equipment, sensors, and ongoing maintenance compared to current low production. This paper presents an innovative method utilizing state-of-art artificial intelligent algorithms to predict the production rate from real-time IIot testing and producing data with very low cost and reliable accuracy. Abundant real-time field and well data acquired from IIot and digital field facilities establish a fundamental foundation for developing a machine learning application. This paper presents a method to predict the real-time production rate from real-time IIot data. In our method, we start with constructing our datasets from different data sources by combining the dynamometer cards, pumping stroke and rate, pump, rod, wellbore and reservoir parameters as inputs and the corresponded production rate as targets. The machine-learning model contains two neural networks: first, a deep autoencoder to extract the feature representations from all the dynamometer cards, then another neural network combining all related features to predict the real-time production. The deep autoencoder derived features from dynamometer cards are used as parts of inputs to further real-time production prediction models, which eliminate the disadvantage of conventional hand-crafted features. Hand-crafted features can lose important information whereas autoencoder is designed to minimize information loss by learning high level features that can be used to reconstruct the cards. The production prediction model with pump and producing data combining more informative abstract features generate good accordance with the history data. After tested testing and validating data in several fields in one operator’s fields in China, the model demonstrates very high accuracy and with R2 more than 0.92, MAE less than 0.5 of wells producing less 5m3/day, RMSE less than 1.6 of wells producing 5-10m3/day and RMSE less than 2.2 of wells producing more than 10m3/day. The model has also been tested on hundreds of newly producing wells and with error in 10%, compared with high resolution real-time metering equipment. The method described in this paper can be fully utilized to metering the real-time production with ultra-low cost in wells as long as acquiring a real-time dynamometer card. With the fast development of artificial intelligence technology and expanding training datasets, artificial intelligence is a good choice to lower the investment and maintenance cost for conventional and unconventional fields in the low oil price trend.

CO

When high temperature and deep carbonate reservoirs are stimulated with conventional acid fracturing fluids, some unfavourable behaviors often occur, such as quick acid-rock reaction, serious damage to downhole string, failure of acid etching in a whole fracture using existing technology whether with prepad or acid system. This paper describes two kinds of new acid fracturing fluid systems which can achieve effective acid etching of a whole fracture, and provides associated fracturing processes. For these acid fracturing systems, SGA-E, an acid generator resistant to high-temperature and friendly to environment, is selected, and supporting high-temperature thickener and cross-linking agent have been developed. The non-crosslinked acid fracturing fluid system with degradable filtrate reducer is applied for tight and low-filtrate loss reservoirs; the cross-linking weak acidic fracturing fluid system stable at high temperature and shear force is applied for reservoirs with developed natural fractures and high filtrate loss. These systems have the advantages of fracturing and acidizing fluids, suitable for reservoirs at 120-160 °C, and can work as prepad for creating fractures.

The effect of reservoir stimulation is not ideal for HPHT fractured reservoir in Tarim Oilfield, so the main factors affecting the reservoir stimulation and the influence rule were studied, then the efficient stimulation measures were proposed. The development degree and effectiveness of natural fractures are key factors. Geomechanical studies have shown that the main factors affecting natural fractures opening including natural fracture occurrence, reservoir stress, pore pressure increment caused by the stimulation, the angle between maximum principal stress direction and natural fracture orientation, cohesion and so on. High angle natural fractures are prone to shear failure; the greater the pore pressure increment during stimulation, the greater the stress difference between the maximum and minimum horizontal stress, natural fractures are more easily open by shear failure. The smaller the angle between natural fracture strike and the maximum principal stress direction, natural fractures shear failure easily occurs. Cohesion has a greater impact on natural fractures opening rate, lower cohesion can greatly increase the natural fractures opening rate. Cohesion drops from 14 MPa to 0 MPa, natural fractures open rate increases from 11.5% to 71.2%, the crack opening rate increased by 59.7%. Reservoir simulation studies have shown that improve the longitudinal producing degree is better than lateral producing degree to improve well productivity. By temporarily blocking natural fractures, improving pump injection, and acidizing fracture fillers to reduce facture cohesion, the fracture opening rate increases, so then the effect of stimulation are improved greatly. The reservoir stimulation countermeasures are an efficient way to yield higher production in ultra-deep fractured reservoir.

To tackle the challenge of great depth, high pressure and high temperature (HPHT), low permeability and porosity, serious heterogeneity in deep oil/gas reservoirs, this paper proposed high efficiency reservoir stimulation based on temporary plugging & diverting, which can realize multi-stage stimulation to improve stimulated rock volume (SRV) without mechanical isolating tools. A series of degradable nanometer ester temporary plugging material with high temperature resistance and high strength has been developed. The feasibility of the method was tested by modeling experiment, and the performance of the plugging material was evaluated. The high efficiency stimulating technology based on chemical plugging & diverting can avoid the problems of high cost and high risk operations by mechanical isolating tools in HPHT deep wells. This technology has been applied in 211 wells from 10 deep oil/gas reservoirs in China, with average production increment per well 3.5 times higher than conventional technologies. This technology enhanced reservoirs production and stimulation effect, lowered the operation difficulty and risk, and decreased multi-stage stimulation cost for deep reservoirs. Key words: deep reservoir, reservoir stimulation, temporary plugging and diverting, high temperature resistance, high strength, degradable chemical plugging material

Double network (DN) hydrogels as one kind of tough gels have attracted extensive attention for their potential applications in biomedical and load-bearing fields. Herein, we import more functions like shape memory into the conventional tough DN hydrogel system. We synthesize the PEG-PDAC/P(AAm-co-AAc) DN hydrogels, of which the first network is a well-defined PEG (polyethylene glycol) network loaded with PDAC (poly(acryloyloxyethyltrimethyl ammonium chloride)) strands, while the second network is formed by copolymerizing AAm (acrylamide) with AAc (acrylic acid) and cross-linker MBAA (N, N'-methylenebisacrylamide). The PEG-PDAC/P(AAm-co-AAc) DN gels exhibits high mechanical strength. The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m3, respectively. Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network, the PEG-PDAC/P(AAm-coAAc) DN hydrogels use P(AAm-co-AAc), a weak polyelectrolyte, as the second network. The AAc units serve as the coordination points with Fe3+ ions and physically crosslink the second network, which realizes the shape memory property activated by the reducing ability of ascorbic acid. Our results indicate that the high mechanical strength and shape memory properties, probably the two most important characters related to the potential application of the hydrogels, can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.

Aimed at the disadvantages of secondary damage to oil layers caused by the conventional bull-heading water control technique, a thermo-sensitive temporary plugging agent for water control was synthesized by water solution polymerization and applied in the field with a new secondary temporary plugging technique. The optimization and performance evaluation of thermo-sensitive temporary plugging agent were carried out through laboratory experiments. The optimized formula is as follows: (6% − 8%) acrylamide + (0.08% − 0.12%) ammonium persulfate + (1.5% − 2.0%) sepiolite + (0.5% − 0.8%) polyethylene glycol diacrylate. The thermo-sensitive temporary plugging agent is suitable for formation temperatures of 70−90 °C, it has high temporary plugging strength (5−40 kPa), controllable degradation time (1−15 d), the apparent viscosity after degradation of less than 100 mPa(S and the permeability recovery value of simulated cores of more than 95%. Based on the research results, secondary temporary plugging technique was used in a horizontal well in the Jidong Oilfield. After treament, the well saw a drop of water cut to 27%, and now it has a water cut of 67%, its daily increased oil production was 4.8 t, and the cumulative oil increment was 750 t, demonstrating that the technique worked well in controlling water production and increasing oil production. Key words: bull-heading water control technique, thermo-sensitive temporary plugging agent, secondary temporary plugging technique, thermal degradation property, reservoir protection

During gel treatments for fractures, the leak-off behavior of gelant has a great effect on the water shut-off performance of gel. Experiments were carried out using a polymer/chromium (Cr3+) gel system to explore the leak-off behavior and the water shut-off performance of gel in fractured media. Results of the gelant leak-off study show that the gelant leak-off from fracture into matrix contributes to the formation of the gelant leak-off layer during the gelant injection. Moreover, because of the gradual formation of the gelant leak-off layer along fracture, the initial leak-off ratio of gelant is relatively high, but it declines and finally levels off with the increase of the injection volume. The polymer concentration of gelant has a great effect on the chromium output in fluids produced from fractures. With the increase of the polymer concentration, the chromium concentration first decreases and then increases, and the leak-off depth of gelant into matrix is gradually reduced. Results of the water shut-off study present that the decrease of the chromium concentration inside the fracture greatly reduces the water shut-off performance after the gel formation. Therefore, because of the relatively high degree of chromium leak-off, enough injection volume of gelant is essential to ensure the sufficient chromium concentration inside the fracture and to further achieve a favorable water shut-off performance. On the premise of gel strength assurance inside the fracture, the water shut-off performance of gel gradually declines with the extension of the distance from the fracture inlet, and different leak-off degrees of gelant along the fracture are responsible for this phenomenon. Therefore, a proper degree of gelant leak-off contributes to enhancing the water shut-off performance of gel for fractures.

CO2-responsive hydrogels, using CO2 as a “green” trigger, have recently been of considerable interest. Herein, a novel CO2-responsive self-healable hydrogel is fabricated by simply mixing hydrophobically modified polyacrylamide (HMPAM) with sodium dodecyl sulfate (SDS)–N,N,N′,N′-tetramethyl-1,3-propanediamine (TMPDA) surfactant micelles. In the presence of CO2, the SDS–TMPDA spherical micelles transform into long wormlike micelles, serving as multivalent cross-linkages to bridge HMPAM chains based on hydrophobic interactions. The interpenetrating three-dimensional network induces a sol-to-gel transition, accompanied by a 360 and 400 times enhancement of the zero-shear viscosity and storage modulus, G′ (ω = 6.28 rad s−1), respectively. In additon, the sol–gel transition can be repeatedly and reversibly switched by cyclically bubbling and removing CO2 without any harm. Furthermore, the CO2-responsive hydrogel exhibits significant shear-thinning and self-healing properties, suggesting that the hydrogel is injectable and could be used as a potential plug to block CO2 gas breakthrough channels during CO2 flooding. Therefore, we believe that the presented work will enable the development of the design of CO2-responsive self-healable hydrogels and their practical applications.

Long-time and over-frequency system power-off of normal intermittent pumping (NIP) technology may exacerbate wax deposit, hydrodynamic level instability and even system restart failure. As a consequence, non-stop-system intermittent pumping (NSSIP) have been tentatively applied into some wells of low production in the oilfield of Daqing in China. Long time pump stop will reduce well production while frequent pumping will lead to void pumping and high energy consumption. Now, NSSIP production scheme is mostly derived from experience decision, the feed and fetch of pump is unbalanced, and it is doubtful whether the low energy consumption characteristic of NSSIP really works. Thus, it is urgent to carry on the research of intelligent scheme optimization for NSSIP. First, based on the A5 database platform of CNPC, the deliberate non-stop pumping scheme and data acquisition scheme for typical experimental wells will be formulated. Second, data of load, displacement and production rate of pumping wells by NSSIP will be measured in real time, then the pump efficiency vs dynamometer working condition will be calibrated accordingly. The CNN+SNN+HDG model is used for training, and the quantitative diagnosis model of insufficient liquid supply is established to realize the intelligent prediction of pump efficiency through dynamometer recognition. Third, the physical model of bottom hole flowing pressure buildup is established to realize the quantitative evaluation of reservoir liquid supply capacity. Data models for correlation analysis of the pumping time, dynamic liquid level, output rate, system efficiency and other parameters are established. At last, the key parameters that affect the pump balance of feed and fetch and economic benefit of a single well are screened out, and the all above analysis results and models are integrated into the ensemble decision tree model to optimize the most reasonable pumping time and frequency for NSSIP schemes. It is suggested that fluid supply capacity affect the intermittent scheme most. For low permeability and low production well, NSSIP could sustain pump fullness, increase system efficiency and save energy at the same time. Based on the ensemble method for intelligently production scheme optimization, NSSIP could increase system efficiency by 7.8% over normal pumping, and by 4.4% over normal intermittent pumping in average. What's more, NSSIP could reduce energy consumption by 22.14 kWh per day compared with intermittent in average. Non-stop-system intermittent pumping (NSSIP) may effectively prevent pumping system from failures caused by void pumping or long-time system stop. It could also greatly increase pumping and system efficiency and save more energy.

The field under study is located in the northern part of Oman where most of the fields have a tight carbonate oil reservoirs. Initially the field was produced under natural depletion for almost 15 years until 2005 when a line drive water flood development with horizontal wells took place and was deployed in the whole field. After more than 10 years of water injection, the water cut reached an average of 75% in the major producing blocks. The reservoir has a light oil with viscosity of 0.8 mPa.s, a downhole temperature of 87°C and average permeability of 10 mD. The calcium and magnesium concentration in formation water is high, about 4000 mg/L. Reservoir heterogeneity in tight carbonate reservoirs causes uneven water flood sweep efficiency and hence resulted in a lot of bypassed oil. The initial EOR methods screening in the field under study didn't recommend to use the conventional polymer flooding due to low reservoir permeability and hence injectivity challenge. However, a new unique nano-ploymer was recently developed in the market to be a potential EOR method for such tight formation reservoirs. Extensive laboratory experiments using the core and fluid samples from the studied reservoir followed by numerical simulation modeling work proved the technical feasibility for this new polymer. This was then followed by field testing pilot in one of the matured water flood sector and the performance is currently under monitoring. The new polymer is a particle-type and comes with various nanometer-micrometer sizes. This polymer has a low apparent viscosity of 1-4 mPa and when it is mixed with the injection water, the particles disperse in the water and the resultant mixture has a low viscosity making it easily to be injected. In addition, this nano-polymer has a high tolerance for both temperature and salinity. While the particles move into formation, they temporarily plug the preferential existing water paths and divert the injection water into the relatively small pores/throats and displace the remaining bypassed oil. The polymer particle has high deformation capacity, so it can deform and pass through the throat under certain pressure to plug even deeper parts of the formation. The process is repeated continuously so that it can inhibit water production and enhance oil production. For the lab experiments, 12 core plugs from the associated reservoir were collected, based on which, a series of experiments were conducted including: core thin section analysis, injectivity test for the nano-polymer and core flooding experiments on single plug and parallel double plugs. Subsequently, the lab results were utilized for numerical simulation and that was followed by economic evaluation. Based on the lab test results, a conceptual simulation model for the studiedfield's sector was used to estimate the incremental oil gain at different pore volume (PV) injection. The incremental oil gain was determined at different SMG PV injection starting from 0.05PV to 0.5 PV. The results showed that the best economic scenario is to go for PV injection of 0.3 which can be achieved in ~4 years'time. However, in order to expedite the field trail stage and reduce its cost, the lowest PV injection was selected which involves injection in two water injectors for one year only at 0.05 PV. The field pilot thus far has successfully completed the injection phase of the total planned volume (0.05 PV) of nano-ploymer. The injection was continuous for one year with no injectivity issue and the production performance is currently under monitoring.

Aiming at deep profile control in water flooding reservoirs, a heterogeneous combination flooding system, composed of dispersed particle gel (DPG) and a zwitterionic surfactant (THSB), was developed. The mean particle diameter was adjusted and then the DPG morphology and diameter were characterized using a laser scanning, confocal microscope, and dynamic light scattering. The effects of DPG concentration, temperature, pH, and salinity on THSB interfacial tension (IFT) were investigated. A method for determining DPG concentration was proposed, and the dynamic retention and migration mechanisms of the particles were evaluated, using cores with different permeabilities. Finally, the oil displacement mechanism of the combination flooding system was explored through visual simulation experiments and scanning electron microscopy. The results showed that the particle diameter was 2.5 μm, that compared with a surfactant system, the IFT of the combination flooding system was relatively low, and that the temperature, pH, and NaCl content of solutions had little effect on THSB IFT. Since specific matching relationships between DPG particles and reservoir pore throats existed, the retention amount and injection pressure were improved with the increased core permeability. Visual simulation experiments revealed the oil recovery mechanism of the combination flooding system. This combination flooding system can be used in reservoirs under varied conditions, as the particle diameter can be adjusted on a case-by-case basis, and the THSB surfactant proved to be high-temperature resistant and high-salinity tolerant. The combination flooding system can be applied to deep profile control and oil displacement, in oil fields.

With the development of many kinds of oilfields, deep well, high deviated well and cluster well are increasing rapidly. Sucker rod pumping still remains a major artificial lift method. There are such problems as severe rod/tubing wearing and shortened rod/tubing life in high deviated rod-pumped wells, and the mechanism and prevention of rod/tubing wearing have not been understood properly. In order to understand the mechanism of rod/tubing wearing, a lateral load calculation model of rod/tubing is solved in this paper. The calculation results show that both the magnitude and direction of lateral force change dynamically with time and space in one stroke cycle. To better describe the rod/tubing wearing phenomenon, the lateral load is divided into two parts: the primary normal vector related to wellbore trajectory and axial force, and the secondary normal vector only related to wellbore trajectory and invariant with time. The three-dimensional and dynamic nature of lateral force can account for the rod/tubing wearing partially. The results of mathematical model show that the magnitude of lateral force at the same depth may differ greatly at different times, and its direction may also change periodically. It is likely to be bidirectional rod/tubing wearing when the primary normal force direction changes periodically. Simulation results show that the direction of lateral force is very likely to change periodically below the neutral point of rod string. This finding has accounted for the common double-faced and multi-faced rod/tubing wearing on the lower rod string. The periodic change of lateral force direction will cause rod/tubing collision, which is also an important cause for the rod/tubing wear below the neutral point. It is assumed qualitatively that the production parameters such as pump depth, stroke, stroke frequency and pump diameter are the major factors of the rod/tubing wearing according to field experience. In this paper, mathematic model is used to analyze the impact of these parameters on lateral force and the quantitative analysis is also conducted which provide theoretical foundation for the design of anti-wear production parameters. The mathematic model and method proposed in this paper are favorable to better accounting for the important phenomenon of rod/tubing wearing in rod-pumped deviated wells. They are capable of the quantitative calculation of lateral forces under different parameter conditions and the anti-wear design. This model has been applied to hundreds of highly deviated wells at Jidong Oilfield, prolonging rod/tubing life 58 days in average.

With the continuously progressing of building the intelligent oilfield and the rapid development of the Internet of Things and big data technology, the emerging technologies such as the information acquisition, distributed computing and data mining lead to unceasing innovation in the research and development and management approaches of enterprises. A technical revolution also occurs in the petroleum industry. At present, one of the challenges we are facing is how to keep the advantages of the conventional oil and gas production and combine them with the big data analysis to better serve the petroleum industry. Given the complexity and uncertainty and thus difficulty endowed in the conventional oil and gas production, one potential solution, based on the big data mining, is proposed. The big data clustering analysis can be used to evaluate the oil well, and the multi-variable sensitivity analysis can be visualized through the dimensionality reduction. Data processing, operation condition diagnosis, forecasting and warning, and multi-dimension multi-variable visualization analysis are the main functions that are achieved in our preliminarily-built big data analysis platform for the production of oil and gas wells. In the future, this platform will gradually transform form the "business model" into the "data model", which adopts various methods such as the statistical analysis, pattern recognition, clustering analysis and visualization to enable the intelligent diagnosis, forecasting and warning, and decision-making optimization and evaluation for the oil and gas well production, on the basis of the multi-dimension multi-source data. At last, it is anticipated that this platform should serve the oil and gas production and evolve into an effective method to improve the production, reduce energy consumption and decrease cost for the oilfield.

To solve the problem of unloading liquid of deep gas well with high gas temperature and salinity, high concentrations of H2S gas and condensate oil, a nanoparticle foam unloading agent was developed and evaluated, and the field test was carried out. The liquid phase foam unloading agent was prepared by blending high temperature resistant anionic surfactant, high salinity and H2S resistant zwitterionic surfactant and the condensate oil resistant fluorocarbon surfactant. The nanoparticle foam unloading agent was developed by introducing silane coupling agent modified nano silica spheres into the liquid phase foam unloading agent as the solid foam stabilizer. The property of nanoparticle foam unloading agent was studied through lab experiment, and the results show that: the agent has temperature resistance as high as 150 °C, salinity resistance up to 250 g/L, H2S resistance up to 0.04%, condensate oil resistance up to 30%, which proves high foaming ability and foam stabilizing ability. The optical microscope and zeta potential results show that: the mechanism of enhancing the property of liquid phase foam unloading agent by nanoparticles lies in the fact that the nanoparticles can adsorb onto the gas-water interface to form a solid particle film, and it has the best foam stabilizing effect at the modified nano-silica sphere concentration of about 0.002%. The field test results show that: the agent has steady performance, meets the site construction requirements, and can improve the water drainage – gas recovery efficiency in gas well and reduce the cost. Key words: deep gas well, nano-particle, nano silica sphere, foam unloading agent, unloading liquid in gas well

As the deep reservoir development in Tarim oilfield, deep wells and ultra-deep wells increase day by day. Meanwhile, in order to stabilize and increase production, pump depth increases continuously because energy and dynamic liquid level of developed reservoirs decline. As the main lifting way, sucker rod pump wells accounts for 65% of the total number of wells in Tarim oilfield. Pump depth of the deepest rod pumped well reaches 5200 meters in Tarim. During lifting process of deep wells, many problems will appear, such as large load and low pump efficiency, and rod lock similar to gas lock may be aroused in extreme cases deep rod pumped wells can appear even, so a series of lifting equipment and parameters optimization measures are essential to realize efficient lifting of deep wells. Aiming at the existing problems of deep well lifting, oil field adopts a variety of solutions. 1) Because of low density and high strength of carbon fiber rod, it can lower polished rod load and improve safe coefficient. Oilfield has conducted test and application of carbon fiber rod to validate the adaptability of carbon fiber rod in deep wells, and evaluate load shedding and energy saving effect; 2) A new type of no-beam long stroke pumping unit is chosen to replace large beam pumping unit, and the long stroke pumping unit can effectively improve pump efficiency; 3) In deep wells, conventional single lifting technology faces worse working conditions and more faults, while combined relay lifting method integrates the characteristics of two lifting processes to show the advantages of both, which can solve the problems that single lifting technology can't solve. The results of field test and application are as follows: 1) Carbon fiber rod has obvious load shedding ability and the mixing rod (40% of carbon fiber rod ratio) can reduce load by nearly one third compared with steel rod. It also has higher stress intensity. The influence factors of stroke loss are complicated, and the adaptability of carbon fiber rod can be improved by rational optimization of rod string assemblage and production parameters. 2) No-beam long stroke pumping unit has a strong adaptability in deep wells of Tarim oilfield and has completely replaced the conventional beam pumping unit. 3) According to reservoir characteristics, well structure and fluid characteristics, the adaptive analysis of various combination lifting systems is carried out to select the combined lifting method suitable for Tarim oilfield. The design of combined relay lifting method is more complicated than that of conventional single pump system, which includes the design of pump depth interval, relevance and continuity of double pump drainage, and sensitivity analysis and comprehensive adjustment of production parameters of double pump. Tarim oilfield has many deep and ultra-deep wells. Now, a wide variety of new technologies have been successful tested and applied, such as new no-beam pumping unit, HL level high strength sucker rod, carbon fiber sucker rod, lifting way of sucker-rod pump combined with jet pump, and lifting way of sucker-rod pump combined with gas lift.

Mud acid is commonly used in acidizing process to prevent the precipitation of Ca2+ and Mg2+ in Dabei–Keshen area of Tarim fields. However, fines migration is then triggered during this process, which severely damages the formation. The objective of this study is to investigate the cause of fines migration and put forward corresponding measures. In this study, the characteristics between 15%-wt HCl and clays at 150 °C were firstly studied from both macro- and micro-perspectives, including dissolution tests, SEM analysis, ICP-AES analysis, core flooding tests. The results were as follows: (1) Dissolution tests showed that the reactions occurred rapidly in the first 2 h and clays were unstable in HCl. Besides, the order of stability was: kaolinite>smectite>illite>chlorite. (2) ICP-AES analysis showed that CAl increased with time while CSi remained unchanged in residual acid. (3) SEM analysis revealed that the foliated illite was broken into smaller pieces and particles as time increased. (4) Core flooding tests further confirmed that the physically damaged clays whose Al-O octahedral structures were broken were the main cause of fines migration. (5) Self-developed chelating acid ZFJ-CA instead of HCl was effective to alleviate the dissolution ratio of clays, which successfully prevent fines migration. This study firstly reveals the characteristics between HCl and clays at 150 °C, clarifies the cause of fines migration, realizes the prevention of fines migration, and further provides reference for field applications.

According to the development situation of digital oilfield construction and existing problems in our country, a set of Internet of Things remote intelligent monitoring system with comprehensive functions for oil & gas fields has been successfully developed, which can comprehensively sense production objects, such as oil&water wells and oil & gas station, to realize centralized management and control of production data and equipment state information in the production command center, and use production data in real time for effective analysis. This system breaks the information island and technical barriers, establishes data bridge with CNPC information system, creates conditions and provides guarantee for tens of thousands of field engineers to implement of fine management using solid theoretical foundation and advanced technical tools, thereby saving construction investment and operation cost of oil and gas production.