An analysis method of injection and production dynamic transient flow in a gas field storage facility

A dynamic transient flow analysis method considering complex factors such as the cyclic injection and production history in a gas field storage facility was established in view of the limitations of the existing methods for transient flow analysis and the characteristics of the injection-production operation of strongly heterogeneous gas reservoirs, and the corresponding theoretical charts were drawn. In addition, an injection-production dynamic transient flow analysis model named "three points and two stages" suitable for an underground gas storage(UGS) well with alternate working conditions was proposed. The "three points" refer to three time points during cyclic injection and production, namely, the starting point of gas injection for UGS construction, the beginning and ending points of the injection-production analysis stage;and the "two stages" refer to historical flow stage and injection-production analysis stage. The study shows that the dimensionless pseudo-pressure and dimensionless pseudo-pressure integral curves of UGS well flex downward in the early stage of the injection and production process, and the dimensionless pseudo-pressure integral derivative curve is convex during the gas production period and concave during the gas injection period, and the curves under different flow histories have atypical features. The new method present in this paper can analyze transient flow of UGS accurately. The application of this method to typical wells in Hutubi gas storage shows that the new method can fit the pressure history accurately, and obtain reliable parameters and results.

Deriving big geochemical data from high-resolution remote sensing data via machine learning:Application to a tailing storage facility in the Witwatersrand goldfields

Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.

Influence of hysteretic stress path behavior on seal integrity during gas storage operation in a depleted reservoir

In this study,we numerically investigate the influence of hysteretic stress path behavior on the seal integrity during underground gas storage operations in a depleted reservoir.Our study area is the Honor Rancho Underground Storage Facility in Los Angeles County(California,USA),which was converted into an underground gas storage facility in 1975 after 20 years of oil and gas production.In our simulations,the geomechanical behavior of the sand reservoir is modeled using two models:(1)a linear elastic model(non-hysteretic stress path)that does not take into consideration irreversible deformation,and(2)a plastic cap mechanical model which considers changes in rock elastic properties due to irreversible deformations caused by plastic reservoir compaction(hysteretic stress path).It shows that the irreversible compaction of the geological layer over geologic time and during the reservoir depletion can have important consequences on stress tensor orientation and magnitude.Ignoring depletion-induced irreversible compaction can lead to an over-estimation of the calculation of the maximum working reservoir pressure.Moreover,this irreversible compaction may bring the nearby faults closer to reactivation.However,regardless of the two models applied,the geomechanical analysis shows that for the estimated stress conditions applied in this study,the Honor Rancho Underground Storage Facility is being safely operated at pressures much below what would be required to compromise the seal integrity.

The Current Situation and Development of Grain Storage Technologies and Facilities for Chinese Farmers

In China, the quantity of farmer＇s grain storage covers about 40% of the total grain yield every year. While, the losses of farms＇ grain storage are up to 8%, which is due to the lack of grain storage facility and technology. The losses of farmer＇s grain storage could reach nearly 20 million tons every year. In this paper, the current situation and development of grain storage technology and facility for Chinese farmers were presented. And a series of policy and research work for reducing the losses of farms＇ grain storage was introduced. The large scale farmers are now developing quickly in China, the new storage warehouse and mechanized facility should be developed adaptively. So, the new storage technology and policy to meet the need of large scale farmers were also introduced in this paper.

Traditional Arctic native fish storage methods and their role in the sustainable development of the Arctic

The business of the Arctic has received increased attention owing to climate change.However,resource development and the use of waterways threaten the fragile Arctic ecology.The indigenous people of the Arctic have acquired a vast amount of traditional knowledge about coexisting in harmony with nature over the course of many years.Herein,five types of fish storage facilities that are commonly used by Arctic indigenous people and their working mechanisms are described.The traditional knowledge of the Arctic indigenous people is practically applied in Arctic fish storage systems,which are still common,effective,and environmentally friendly.The traditional fish storage facilities of the aborigines are of significance because they promote the sustainable development of the Arctic.

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.

Marketing of African Walnut [Tetracarpidium conophorum Mull. （Arg）] in Southwest Nigeria： Production Issues and Contributions to Stakeholders

The survey was carried out in Ekiti, Oyo, Ogun, Ondo, and Osun states located in SW Nigeria. The respondents for the study include thirty marketers and thirty producers of African walnut randomly selected within each of the state, making a total of 60 respondents per state and 300 for the five states. Two sets of structured questionnaires were designed, one for each eatergory of the respondents. The questionnaires were designed to solicit information on demographic characteristics of the producers and marketers of African walnut, production and marketing challenges of the walnut, price trend along the marketing chain, and interraction and relationships of the middlemen. The questionnaires were administered through individual and focus group methods. Also, indepth interview of the respondents was conducted to supplement data obtained from the questionnaires. Purposive sampling method was used to select local markets where the walnut were sold for study of the price trend of the walnut for two seasons （from 2007 to 2008）. Marketing of the walnut started with farmers that plant the perennial climber on their farmland, while the marketing intermediaries include the village merchants, wholesalers, and the retailers. Adult male dominated the production sector of African walnut while processing and marketing the nuts were mostly done by the women and children. There is high demand for the walnut as delicacy and snack; although industrial usage is yet to be fully developed. Marketing of the cooked nuts and at retail quantity had the highest profit along the marketing chain. There is need for expansion of the current scale of production of the walnut to meet increasing demand. Provision of appropriate storage facilities to prevent spoilage of the product in rural areas and good rural road network for easy conveyance to urban markets where it is majorly consumed is pertinent for marketing of the walnut.

A new method for the extraction of tailing ponds from very high-resolution remotely sensed images:PSVED

Automatic extraction of tailing ponds from Very High-Resolution(VHR)remotely sensed images is vital for mineral resource management.This study proposes a Pseudo-Siamese Visual Geometry Group Encoder-Decoder network(PSVED)to achieve high accuracy tailing ponds extraction from VHR images.First,handcrafted feature(HCF)images are calculated from VHR images based on the index calculation algorithm,highlighting the tailing ponds'signals.Second,considering the information gap between VHR images and HCF images,the Pseudo-Siamese Visual Geometry Group(Pseudo-Siamese VGG)is utilized to extract independent and representative deep semantic features from VHR images and HCF images,respectively.Third,the deep supervision mechanism is attached to handle the optimization problem of gradients vanishing or exploding.A self-made tailing ponds extraction dataset(TPSet)produced with the Gaofen-6 images of part of Hebei province,China,was employed to conduct experiments.The results show that the proposed'method_achieves the best visual performance and accuracy for tailing ponds extraction in all the tested methods,whereas the running time of the proposed method maintains at the same level as other methods.This study has practical significance in automatically extracting tailing ponds from VHR images which is beneficial to tailing ponds management and monitoring.