Remaining oil distribution characteristics in an oil reservoir with ultra-high water-cut

An accurate mapping and understanding of remaining oil distribution is very important for water control and stabilize oil production of mature oilfields in ultra-high water-cut stage.Currently,the Tuo-21 Fault Block of the Shengtuo Oilfield has entered the stage of ultra-high water cut(97.2%).Poor adaptability of the well pattern,ineffective water injection cycle and low efficiency of engineering measures(such as workover,re-perforation and utilization of high-capacity pumps)are the significant problems in the ultra-high water-cut reservoir.In order to accurately describe the oil and water flow characteristics,relative permeability curves at high water injection multiple(injected pore volume)and a semiquantitative method is applied to perform fine reservoir simulation of the Sand group 3e7 in the Block.An accurate reservoir model is built and history matching is performed.The distribution characteristics of remaining oil in lateral and vertical directions are quantitatively simulated and analyzed.The results show that the numerical simulation considering relative permeability at high injection multiple can reflect truly the remaining oil distribution characteristics after water flooding in an ultrahigh water-cut stage.The distribution of remaining oil saturation can be mapped more accurately and quantitatively by using the‘four-points and five-types’classification method,providing a basis for potential tapping of various remaining oil types of oil reservoirs in late-stage of development with high water-cut.

Production prediction at ultra-high water cut stage via Recurrent Neural Network

A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were carried out.Since the traditional Fully Connected Neural Network(FCNN)is incapable of preserving the correlation of time series data,the Long Short-Term Memory(LSTM)network,which is a kind of Recurrent Neural Network(RNN),was utilized to establish a model for oil field production prediction.By this model,oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time.Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage,and the results were compared with the results from the traditional FCNN and water drive characteristic curves.The LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field production.The LSTM model was used to predict the monthly oil production of another two oil fields.The prediction results are good,which verifies the versatility of the method.

Flexible,durable,and anti-fouling nanocellulose-based membrane functionalized by block copolymer with ultra-high flux and efficiency for oil-in-water emulsions separation

The clearwater obtained from stabilized oily wastewater has become a worldwide challenge.Nowdays,the area of oil/water emulsion separation materials have accomplished great progress,but still faces the enormous problems of low flux,poor stability,and pollution resistance.Nanocelluloses(cellulose nanocrystals(CNC))with the advantages of hydrophilicity,ecofriendliness,and regeneration are ideal materials for the construction of separation membranes.In this paper,a flexible,antifouling,and durable nanocellulose-based membrane functionalized by block copolymer(poly(N-isopropylacrylamide)-b-poly(N,Ndimethylaminoethyl methacrylate))is prepared via chemical modification and self-assembly,showing high separation efficiency(above 99.6%)for stabilized oil-in-water emulsions,excellent anti-fouling and cycling stability,high-temperature resistance,and acid and alkali resistance.More importantly,the composite membrane has ultra-high flux in separating oil-in-water emulsions(29,003 L·m^(−2)·h^(−1)·bar^(−1))and oil/water mixture(51,444 L·m^(−2)·h^(−1)·bar^(−1)),which ensures high separation efficiency.With its durability,easy scale-up,and green regeneration,we envision this biomass-derived membrane will be an alternative to the existing commercial filter membrane in environmental remediation.

大采高超高水材料分体充填技术在邢东矿的应用

邢东矿在其井田范围内共11个村压,人口密集,村庄搬迁难度大,周期长,且投入成本高,严重影响矿山正常生产接替。为了有效降低煤炭开采平均成本,解决村庄压滞煤炭资源开采的问题,邢东矿在1126工作面回采时首先提出并应用综合机械化超高水材料充填开采技术,在1128工作面回采过程中将这套技术不断完善,并取得了可观的经济效益和社会效益。

Optimization of submerged entry nozzle parameters for ultra-high casting speed continuous casting mold of billet

Controlling the flow behavior in the mold in an appropriate way is the basis for realizing the billet ultra-high speed continuous casting.Based on the new proposed physical water modeling experiment considering the effects of solidified shell and hydrostatic pressure,the flow behavior in the mold with cross section of 160 mm 9160 mm during continuous casting of billet is regulated by optimizing the inner diameters and immersion depths of submerged entry nozzle at the ultra-high casting speeds of 5.0–6.5 m/min.The results show that under the premise of no slag entrainment,as well as uniform coverage and keeping good fluidity of liquid slag layer on the top free surface of the fluid in the mold,the appropriate parameters of submerged entry nozzle under the ultra-high casting speed of billet are 50 mm in inner diameter,95 mm in outer diameter and 180 mm in immersion depth.And on the basis of the obtained parameters of submerged entry nozzle,it can be known that the reasonable ranges of level fluctuation and impacting depth of the stream in the mold are about 0.82-1.11 and 593-617 mm,respectively.