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Coupling Long-Range Autonomous Underwater Vehicles (LRAUVs) with Unmanned Surface Vehicles (USVs) solves two of the key challenges associated with LRAUV missions: lack of real-time communication with the underwater asset and unbounded navigational error growth from dead reckoning. The coupling of LRAUVs and USVs effectively transforms the capabilities and accuracy of the LRAUV survey.
A premier supplier of Unmanned and Autonomous Marine Systems led this development project working alongside a world-leading research center and developer of LRAUV systems. These two organizations were assisted by a leading developer of subsea acoustic positioning, communications and sonar systems, and a developer of software solutions for autonomous systems.
The system architecture enables the USV to provide regular position updates to the LRAUV, removing the need for the LRAUV to surface from depth to update its internally calculated position. This cooperative localization scheme increases the efficiency and accuracy of LRAUV survey while reducing cost. The combination of the high-accuracy sonar systems on the LRAUV transiting close to the seabed and accurate position updates from the USV provides game-changing solutions for deep water surveys and Exclusive Economic Zone (EEZ) mapping globally.
Due to the endurance and autonomy, this combination also allows for the possibility of executing remote subsea operations from a shore-based location. Eliminating the need for large ships to accompany the LRAUV significantly reduces data acquisition costs.
The USV communicates with the LRAUV through two key methods: acoustics to provide short mission updates and positioning information, and optical communication technology to enable the system to upload the data from the survey sensors. With the data uploaded to the USV, it is then possible for the USV to process the data to enable summary data to be passed back through satellite or radio communications to a control center. In situations where data may indicate where gaps occur, or further investigation is required, an updated mission plan can be transmitted from the control center to the USV and then to the LRAUV. As onboard data processing techniques improve, the USV can be used to adaptively update the LRAUV's mission without human intervention.
This transition to autonomy will save costs, reduce risk, and increase flexibility across a range of applications, including mine countermeasures, weapons testing, hydrography, environmental science, security, and surveillance.
Low-vacuum scanning electron microscopy (SEM) / energy dispersive x-ray (EDX) analysis can be used to characterize the nature of inorganic scale from produced water (Method 1); routinely used to visually determine the degree, form and composition of scale particulates. Quantitative data on scale coverage can be extracted through image analysis, and morphology can indicate origins of particulates (transported scale, active scale…). Recent trends demand more detailed quantitative analysis, believed to produce more accurate / reproducible results. Such a method is automated SEM-EDX particle analysis (Method 2). This has the advantage of full automation and delivers quantitative data on scale coverage, composition, shape and size. Neither method is perfect, the first relies on experienced SEM users, is a manual method, susceptible to bias, and is often perceived as producing qualitative data, while the second method although producing large quantitative data sets, depends upon the criteria used to classify particles, and can be time consuming. Both methods were used to examine a number of filtered produced water samples. The traditional manual method provides good representative results on scale coverage, details on particulate morphology and composition, and can be undertaken in about thirty minutes per sample; it is also a simple matter to differentiate between particulate and blanket scale deposits. The second method generates superior levels of quantitative data, but results are dependent on image thresholding (for particle selection), erroneous misleading results are all too easily obtained (unless rigorously tested particle classification schemes are used), and the method can take in excess of an hour per sample. In general Method 1 should be adequate to track scale issues from produced water, which can be supplemented where desired by automated particle analysis (APA). Where APA is to be used it is recommended that an industry standard classification criterion be developed, which will increase the degree of confidence that can be applied to results, and allow direct comparison of results between laboratories.
Abstract This paper presents a new method for modelling and operation optimization of large scale water injection systems. Systems of this kind usually consist of a great number of water injection stations, pipelines, valves, pipe fittings, and water injection wells, and is a multi-source multi-sink nonlinear time-varying system with thousands of variables. A properly simplified and sufficiently accurate mathematical model has been made by both the analytical method and the system identification method. A new operation optimization method has been developed for this system by the large scale system theory and the dynamic programming method. To solve this optimization problem, the concept of pressure valley in hydrodynamic system is presented for the first time in this paper. The water injection system is divided into a series of subsystems according to pressure valleys. It has been proved that system optimization on the whole is equivalent to optimization of every subsystem in it if coupling variables of the subsystems have the values resulting from the optimized system. Thus, the system optimization problem is decomposed into a series of subsystem optimization problems. The above modeling method and operation optimization procedures are programmed in a software package. This package has been applied to an oilfield in Daqing. Electric power consumption for water injection decreased by 3-10%, and waterflood efficiency improved. Introduction Water injection is one of most important tasks in waterflooded fields. The operational status of water injection system will have a large influence on the production costs, especially for a high water-cut field. In fact, the electric power consumption (EPC) rate of oil production rapidly increases with water-cut (see fig. 1), and the EPC of water injection also steeply rises along with water-cut (see curve 2, fig. 1). When water-cut is greater than 75%, the ratio of EPC of water injection to total EPC of oil production will be over 40%. Operation optimization of water injection system can considerably reduce EPC. Obviously, proper pressure and flowrate distributions in a water injection system will help to realize an optimum waterflood policy, raise waterflood efficiency, control increasing rate of water-cut and enhance recovery efficiency. Because a large scale water injection system usually consists of a great number of water injection stations, pipelines, valves, fitting parts, and water injection wells, and is a multi-source, multi-sink, nonlinear time-varying system with thousands or even more variables, such a extremely complex system is difficult to control and optimize. We present a modelling method of water injection system by both analytical method and system identification method. After carefully researching dynamic behavior of the system, we present the concept of "pressure valley." Applying this concept and large scale system theory, we develop a new operation optimization method for the water injection system. These methods have been programmed in a software package. Section II presents models of the water injection system and its fundamental components.
Abstract Determination of original hydrocarbon in place, OHIP, is a vital task in petroleum development. The estimation of appropriate rock and fluid properties is a requirement, with irreducible water saturation, Swir, being one of the key parameters. A representative Swir is also required when conducting multi-phase experiments, for example relative permeability determination. Furthermore, Swir has an influence on residual oil saturation during tertiary recovery. Conducting primary drainage capillary pressure experiments to measure Swir is the industry-established practise. Such experiments tend to require considerable resources and long time periods to complete. As a consequence, a limited number of representative core plugs are typically considered, often leading to data gaps for some facies within a reservoir. In such situations, an empirical model may be useful in predicting Swir. In a recent study, artificial neural networks have been applied successfully to the estimation of irreducible water saturation (Swir) for Australian formations. The model demonstrates a superior performance when compared with other, conventional models. This paper features the translation of the artificial neural network model into a simple mathematical equation that is suitable for quick hand calculation. Moreover, a new semi-empirical model to predict Swir is presented, containing five adjustable constants. The optimal values for these constants were obtained by minimizing the calculated error utilizing a genetic algorithm. Both neural network and semi-empirical models were developed, calibrated and validated by using an extensive data set gathered for Australian hydrocarbon basins. Background The accurate determination of irreducible water saturation, (Swir), is important in reservoir engineering and petrophysical calculations, for example:Volumetrics, in determining initial oil (or gas) saturation, (Soi or Sgi) Estimation of residual oil saturation (Sor), which is the target for tertiary recovery Base parameter for relative permeability end-point, (komax) Parameter in rock wettability consideration, for example Craig's criteria . It should be mentioned that in petroleum engineering irreducible water saturation (Swir) and initial water saturation (Swi) are sometimes used interchangeably. This fact may be due to a misuse or misunderstanding of terms. The capillary pressure vs. saturation curve generated from a capillary pressure experiments, after translation to reservoir conditions, defines the saturation profile across a reservoir, and in an ideal situation corresponds to the actual saturation profile encountered in the reservoir (obtained from log analysis), representing connate (or geological) water. A laboratory profile, representing the initial water saturation profile may thus be transformed into a saturation versus height relationship. Swir, on the other hand, is a limiting value that is taken as a characteristic value for a particular reservoir. Considering a capillary pressure curve, Swir is the lowest water saturation value corresponding to the maximum capillary pressure value, and should be equivalent to that encountered at the reservoir crest.
A private company based in Houston, Texas, collects and analyzes water source, logistics, recycling and disposal data for the upstream oil and gas industry. The company recently started applying machine learning methods to satellite imagery of the Permian Basin to identify frac water impoundments and facilitate water trading between operators. Freshwater frac pond locations in Texas are not available from any public record data source, so identification of their locations and other characteristics through satellite imagery analysis may reveal new insights about energy industry operations. The company partnered with the University of Texas Bureau of Economic Geology to analyze eleven quarters of historical central Midland Basin satellite imagery, a time series beginning in January 2016, to test correlations between frac ponds and hydraulic fracturing activity. The study identified a significant positive correlation between total frac pond surface area in the satellite imagery and IHS data on aggregate water use for hydraulic fracturing in the Midland Basin area studied. This study also found a striking 76% increase in average frac pond volume over the 27-month study period, and found that the percentage of available frac pond water injected for hydraulic fracturing grew from 11% to 20% from January 2016 to July 2017. The most notable observation from this study, however, may be that the permit-based (IHS) control data appeared to lose integrity between six and ten months prior to the study date, while the satellite-derived data appeared to maintain integrity right up to the study date. As of May 2018, the IHS data shows water injected for hydraulic fracturing in the Midland Basin dropping 90% beginning in July 2017 because of absence of complete data. The satellite data, however, shows steady growth in total frac water supply throughout that period. This timeliness-of-data comparison was not the purpose of the study, but the study conclusions were limited because reliable control data could not be obtained for the time period of about ten months prior to the study date. This suggests that oilfield market research could be improved through the use of more satellite imagery analytics versus public permit and industy reported data.