mwirth-epo/epo-nmt-datasets huggingface dataset:
例如，在实施方案中，在步骤302处，可以向神经网络108提供标记的训练数据集，该标记的训练数据集包括已知健康状态110(图14)的一对或多对参数读数107(图7和图17)，例如由梯度线圈86、88和/或90生成的与每个记录/采集/采样的声学频率的一组对应的已知/预期健康得分配对的一组记录/采集/采样的声学频率。
For example, in embodiments, the neural network 108 may be provided, at step 302, a labeled training dataset that includes one or more pairings of a parameter reading 107 (FIGS. 7 and 17 ) to a known health status 110 (FIG. 14 ), e.g., a set of recorded/acquired/sampled acoustic frequencies generated by the gradient coil 86, 88, and/or 90 paired to a corresponding set of known/expected health scores for each of the recorded/acquired/sampled acoustic frequencies.
在实施方案中，在步骤304处，可以针对训练数据集内的每个配对经由神经网络108生成健康状态，并且在步骤306处，将其与相同配对的已知健康状态进行比较。
In embodiments, a health status may be generated via the neural network 108, at step 304, for each pairing within the training dataset, and compared, at step 306, to the known health status of the same pairing.
如果所生成的健康状态分别与配对的已知健康状态匹配或不匹配，则在步骤308处，可以增强神经网络108的权重，或者在步骤310处，可以调整该神经网络的权重。
The weights of the neural network 108 may then be reinforced, at step 308, or adjusted, at step 310, if the generated health status respectively matches or fails to match the known health status of the pairing.
如决策框312所示，可以对训练数据集中的所有配对重复执行步骤304、306、308和310多次，直到神经网络108的准确性达到足够的水平，例如，一对训练数据集中神经网络108针对给定参数读数107生成的健康状态与同一配对的已知健康状态在百分之九十九(99％)的时间内匹配。
As represented by decision block 312, steps 304, 306, 308, and 310 may be repeated for all pairings in the training dataset multiple times until the accuracy of the neural network 108 reaches a sufficient level, e.g., the health status generated by the neural network 108 for a given parameter reading 107 in a pairing of the training dataset matches the known health status of the same paring ninety-nine percent (99%) of the time.
虽然上面的段落讨论了经由监督方法训练神经网络108，但是如将理解的那样，也可以采用其他训练神经网络108的方法，例如无监督学习。
While the above paragraphs discuss training the neural network 108 via supervised methods, as will be appreciated, other methods of training the neural network 108 may be employed, e.g., unsupervised learning.
如本文所用，术语“无监督学习”是指在没有已知输出的情况下训练神经网络108的权重的过程。
As used herein, the term “unsupervised learning” refers to a process of training the weights of the neural network 108 without known outputs.
例如，在此类实施方案中，神经网络108可被配置为训练权重，以便最大化成本函数。
For example, in such embodiments, the neural network 108 may be configured to train the weights so as to maximize a cost function.
转到图17，可以在从通常由162、164、166表示的多个梯度线圈得到的数据集上训练神经网络108(图12)的实例，所述多个梯度线圈可被设置在通常由168、170、172表示的不同MRI系统中，该不同MRI系统位于世界各地经由网络174例如互联网连接到位于单独站点178例如MRI研究中心和/或实验室处的服务器176的不同操作设施例如医院和/或实验室处。
Moving to FIG. 17, an instance of the neural network 108 (FIG. 12 ) may be trained on a dataset derived from a plurality of gradient coils, generally represented by 162, 164, 166, which may be disposed in different MRI systems, generally represented by 168, 170, 172, located at different operating facilities, e.g., hospitals and/or labs, across the world, connected via a network 174, e.g., the Internet, to a server 176 located at a separate site 178, e.g., a MRI research center and/or laboratory.
如将理解的那样，MRI系统168、170、172中的每个MRI系统可包括系统96(图7)的实施方案，使得训练数据集可包括经由如上所述的传感器从梯度线圈162、164、166采集的参数读数107(图7和图17)，以及相同梯度线圈162、164、166和/或脉冲序列180的失效日期/时间的对应列表。
As will be appreciated, each of the MRI systems 168, 170, 172 may include embodiments of the system 96 (FIG. 7 ) such that the training dataset may include parameter readings 107 (FIGS. 7 and 17 ) acquired from the gradient coils 162, 164, 166 via sensors as described above, and a corresponding list of the date/times of failures of the same gradient coils 162, 164, 166 and/or pulse sequences 180.
然后数据集可发送到服务器176、存储在服务器中或以其他方式可通过服务器访问。
The dataset may then be sent to, stored in, or otherwise accessible by the server 176.
然后可以在服务器176上用数据集来训练神经网络108的实例，以便确定/发现所采集的指示梯度线圈失效的参数读数107(图7和图17)内的关系/症状。
An instantiation of the neural network 108 may then be trained on the server 176 with the dataset in order to determine/discover relationships/symptoms within the acquired parameter readings 107 (FIGS. 7 and 17 ) indicative of gradient coil failures.
如将理解的那样，然后可将在服务器176上训练的实例化神经网络的权重发送回MRI系统168、170、172中的每个MRI系统的系统96。
As will be appreciated, the weights of the instantiated neural network trained on the server 176 may then be sent back to the system 96 in each of the MRI systems 168, 170, 172.
换句话讲，历史数据例如在给定的持续时间内从多个MRI系统168、170、172的梯度线圈162、164、166获得的参数读数107(图7和图17)可经由神经网络108趋势化，以检测/识别相同或相似的梯度线圈162、164、166内的即将发生的失效。
In other words, the historical data, e.g., parameter readings 107 (FIGS. 7 and 17 ) obtained from the gradient coils 162, 164, 166 of multiple MRI systems 168, 170, 172 over a given duration of time, may be trended via the neural network 108 to detect/identify impending failures within the same, or similar, gradient coils 162, 164, 166.
例如，可以在MRI系统168和170变为在线(即，开始操作使用)时或附近，从一个或多个MRI系统168和170的梯度线圈162、164获得参数读数107(图7和图17)，并将其作为历史数据存储在服务器176中。
For example, parameter readings 107 (FIGS. 7 and 17 ) may be obtained from the gradient coils 162, 164 of one or more MRI systems 168 and 170 at, or near, the time the MRI systems 168 and 170 come online, i.e., begin operational use, and stored as historical data in the server 176.
然后可以训练神经网络108以识别给定参数读数107的典型范围，该范围对应于梯度线圈使用寿命周期(即，从首次使用梯度线圈到失效的时间)的开始。
The neural network 108 may then be trained to recognize typical ranges for a given parameter reading 107 that correspond to the beginning of a gradient coil life cycle, i.e., the time from first use of a gradient coil until failure.
随着时间的推移，可以从MRI系统168和170中的梯度线圈162、164获得其他参数读数107，并将其存储在服务器176中，以便可以训练神经网络108来识别参数读数107在梯度线圈的整个使用寿命周期中如何变化，直至和/或包括梯度线圈的失效。
Over time, additional parameter readings 107 may be obtained from the gradient coils 162, 164 in the MRI systems 168 and 170 and stored in the server 176 so that the neural network 108 may be trained to recognize how a parameter reading 107 changes during the life cycle of a gradient coil, up to and/or including, failure of the gradient coil.
如将理解的那样，随着新的/更年轻的MRI系统例如MRI系统172在神经网络108已经有机会在包含更旧的MRI系统168和170的一个或多个梯度线圈162、164的完整使用寿命周期的参数读数107(图7和17)上进行训练之后变为在线，新的/更年轻的MRI系统172的梯度线圈166的健康状态可经由在新系统172本地操作的神经网络108的实例，使用从更旧的梯度线圈162和164的使用寿命周期获得/学习的训练结果/权重，在其自身使用寿命周期的各个时间点重复生成。
As will be appreciated, as new/younger MRI systems, e.g., MRI system 172, come online after the neural network 108 has had the opportunity to be trained on parameter readings 107 (FIGS. 7 and 17 ) encompassing a full life cycle of one or more gradient coils 162, 164 of the older MRI systems 168 and 170, the health status of the gradient coils 166 of the new/younger MRI systems 172 may be repeatedly generated at various points in their own life cycles via instances of the neural network 108 operating locally at the new systems 172 using training results/weights obtained/learned from the life cycles of the older gradient coils 162 and 164.
例如，在此类实施方案中，健康状态110可以是在零(0)至十(10)的范围内的得分，其中神经网络108为新的/更年轻的MRI系统172中的参数读数107(图7和图17)符合梯度线圈166的使用寿命周期中特定时间点的历史标准的梯度线圈166生成/分配十(10)，如经由神经网络108通过使用从较旧的MRI系统168和170的梯度线圈162和164采集的历史数据集/参数读数107进行的训练所了解/学习的那样。
For example, in such embodiments, the health status 110 may be a score that ranges from zero (0) to ten (10) where the neural network 108 generates/assigns a ten (10) for a gradient coil 166 in a new/younger MRI system 172 with parameter readings 107 (FIGS. 7 and 17 ) that are in accordance with historical norms for the particular point in the gradient coil's 166 life cycle, as understood/learned via the neural network 108 from training with the historical dataset/parameter readings 107 acquired from the gradient coils 162 and 164 of the older MRI systems 168 and 170.
如将会理解的那样，在此类实施方案中，神经网络108可以生成逐渐降低的得分，该得分对应于在新的/更年轻的MRI系统172中的梯度线圈166的参数读数107偏离历史标准的量，其中零(0)为可检测到的最大偏差量。
As will be understood, in such embodiments, the neural network 108 may generate progressively lower scores corresponding to the amount that the parameter readings 107 of the gradient coil 166 in the new/younger MRI system 172 deviate from the historical norms, with zero (0) being the maximum amount of detectable deviation.
换句话讲，在此类实施方案中，健康状态/得分110(图10)为十(10)指示梯度线圈166老化，即导致结构劣化，如基于历史标准所预期的那样，其中健康状态/得分为零(0)指示梯度线圈166的老化速度快于基于历史标准的预期值。
In other words, in such embodiments, a health status/score 110 (FIG. 10 ) of ten (10) indicates that the gradient coil 166 is aging, i.e., incurring structural degradation, as expected based on historical norms, where a health status/score of zero (0) indicates that the gradient coil 166 is aging faster than expected based on historical norms.
在此类实施方案中，较低阈值154(图14)可为五分(5)，该较低阈值可指示应当更换梯度线圈，并且/或者应当采取纠正措施来减轻/降低失效的风险，例如在未来的操作周期中降低梯度线圈的温度加速度和/或使用软件互锁来阻止可能增加结构劣化的激发频率。
In such embodiments, the lower threshold 154 (FIG. 14 ) may be a score of five (5), which may indicate that the gradient coil should be replaced, and/or that corrective action should be taken to mitigate/reduce the risk of failure, e.g., a reduction in the temperature acceleration of the gradient coil during future operational cycles and/or the use of software interlocks to block-out excitation frequencies that may increase structural degradation.
另外，在实施方案中，神经网络108可以提供梯度线圈加速度(即，物理振动和/或声学)与梯度线圈失效之间的相关性；以及类似地，电感-电阻(“LR”)和反EMF与梯度线圈失效之间的相关性。
Additionally, in embodiments, the neural network 108 may provide for a correlation between gradient coil acceleration, i.e., physical vibrations, and/or acoustics, and gradient coil failure; and similarly, for a correlation between inductance-and-resistance (“LR”) and back EMF and gradient coil failure.
如将理解的那样，虽然上述训练场景涉及从多个MRI系统168和170获得的参数读数107(图7和图17)，但是应当理解，在实施方案中，用于训练神经网络108的权重的数据集可以得自单个MRI系统168的单个梯度线圈86、88和/或90。
As will be appreciated, while the above described training scenario concerned parameter readings 107 (FIGS. 7 and 17 ) obtained from multiple MRI systems 168 and 170, it will be understood that, in embodiments, the dataset used to train the weights of the neural network 108 may be derived from a single gradient coil 86, 88, and/or 90 of a single MRI system 168.
重新参见图15，在实施方案中，方法146还可包括在步骤182处，预测健康状态110(图14)将超过较低阈值154(图14)的时间段184(图14)。
Referring back to FIG. 15, in embodiments, the method 146 may further include predicting, at step 182, a time period 184 (FIG. 14 ) when the health status 110 (FIG. 14 ) will exceed the lower threshold 154 (FIG. 14 ).
例如，可以如上所述训练神经网络108，使得神经网络108能够基于所采集的参数读数107(图7和图17)准确地预测梯度线圈86、88和/或90将具有足以保证修复和/或更换梯度线圈86、88和/或90的持续结构劣化的未来时间/日期。
For example, the neural network 108 may be trained as disclosed above such that the neural network 108 is able to accurately predict, based on the acquired parameter readings 107 (FIGS. 7 and 17 ), a future time/date that the gradient coil 86, 88, and/or 90 will have sustained structural degradation sufficient to warrant repair and/or replacement of the gradient coil 86, 88, and/or 90.
时间段184可以各种格式显示在监视器18上(如图14所示)，所述各种格式例如周、年、天、小时、分钟、秒、标准日期格式和/或能够传达在健康状态110超过较低阈值154之前的剩余时间量的任何其他格式。
The time period 184 may be displayed on the monitor 18 (as shown in FIG. 14 ) in various formats, e.g., weeks, years, days, hours, minutes, seconds, standard date formats, and/or any other format capable of conveying the amount of time remaining until the health status 110 exceeds the lower threshold 154.
最后，还应当理解，系统10和/或96可包括必要的电子器件、软件、存储器、存储装置、数据库、固件、逻辑/状态机、微处理器、通信链路、显示器或其他视觉或音频用户界面、打印设备以及用于执行本文所述功能和/或实现本文所述结果的任何其他输入/输出接口。
Finally, it is also to be understood that the systems 10 and/or 96 may include the necessary electronics, software, memory, storage, databases, firmware, logic/state machines, microprocessors, communication links, displays or other visual or audio user interfaces, printing devices, and any other input/output interfaces to perform the functions described herein and/or to achieve the results described herein.
例如，如前所述，系统10和/或96可包括至少一个处理器和系统存储器/数据存储结构，所述系统存储器/数据存储结构可包括随机存取存储器(RAM)和只读存储器(ROM)。
For example, as previously mentioned, the systems 10 and/or 96 may include at least one processor and system memory/data storage structures, which may include random access memory (RAM) and read-only memory (ROM).
系统10和/或96的至少一个处理器可包括一个或多个传统微处理器和一个或多个辅助协处理器，诸如数学协处理器等。
The at least one processor of the systems 10 and/or 96 may include one or more conventional microprocessors and one or more supplementary co-processors such as math co-processors or the like.
本文讨论的数据存储结构可以包括磁性、光学和/或半导体存储器的适当组合，并且可以包括例如RAM、ROM、闪存驱动器、诸如压缩盘的光盘和/或硬盘或驱动器。
The data storage structures discussed herein may include an appropriate combination of magnetic, optical and/or semiconductor memory, and may include, for example, RAM, ROM, flash drive, an optical disc such as a compact disc and/or a hard disk or drive.
另外，可以从计算机可读介质中将使控制器适于执行本文所公开的方法的软件应用程序读取到至少一个处理器的主存储器中，所述计算机可读介质例如向系统10和/或96的至少一个处理器(或本文所述的设备的任何其他处理器)提供或参与提供指令以供执行的介质。
Additionally, a software application that adapts a controller to perform the methods disclosed herein may be read into a main memory of the at least one processor from a computer readable medium, e.g., a medium that provides or participates in providing instructions to the at least one processor of the systems 10 and/or 96 (or any other processor of a device described herein) for execution.
此类介质可以采用许多形式，包括但不限于非易失性介质和易失性介质。
Such a medium may take many forms, including but not limited to, non-volatile media and volatile media.
虽然在实施方案中，软件应用程序中的指令序列的执行致使至少一个处理器执行本文所述的方法/过程，但可以使用硬连线电路代替软件指令或与软件指令相结合以用于实现本发明的方法/过程。
While in embodiments, the execution of sequences of instructions in the software application causes at least one processor to perform the methods/processes described herein, hard-wired circuitry may be used in place of, or in combination with, software instructions for implementation of the methods/processes of the present invention.
因此，本发明的实施方案不限于硬件和/或软件的任何特定组合。
Therefore, embodiments of the present invention are not limited to any specific combination of hardware and/or software.
应当进一步理解，以上描述旨在是例示性的而非限制性的。
It is further to be understood that the above description is intended to be illustrative, and not restrictive.
例如，上述实施方案(和/或其方面)可以彼此组合使用。
For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other.
另外，在不脱离本发明的范围的情况下，可以进行许多修改以使特定情况或材料适应本发明的教导。
Additionally, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope.
因此，通过提供对梯度线圈的健康状态的自动监测，本发明的一些实施方案可以降低梯度线圈在MRI扫描期间失效的风险，而无需手动检查梯度线圈。
Accordingly, by providing automated monitoring of a health status of a gradient coil, some embodiments of the present invention may reduce the risk of the gradient coil failing during an MRI scan without the need to manually inspect the gradient coil.
如将理解的那样，此类实施方案可通过避免由于梯度线圈失效而需要重新扫描患者，以及减少和/或避免在手动检查梯度线圈期间导致的MRI系统的停机时间，来提高MRI系统的患者吞吐量。
As will be appreciated, such embodiments may improve the patient throughput of an MRI system by avoiding the need to rescan a patient due to a failed gradient coil, as well as reducing and/or avoiding down time of an MRI system incurred during manual inspection of the gradient coils.
另外，通过预测梯度线圈预期失效的时间段，本发明的一些实施方案可通过促进调度患者扫描和由于梯度线圈维护/更换而导致的MRI系统停机时间之间的改进的协调来提供比传统MRI系统更高的患者吞吐量。
Further, by predicting a time period during which a gradient coil is expected to fail, some embodiments of the present invention may provide for improved patient throughput over traditional MRI systems by facilitating improved coordination between scheduling patient scans and MRI system down time due to gradient coil maintenance/replacement.
换句话讲，与反应性地检测已经失效的梯度线圈不同，本发明的一些实施方案提供了对梯度线圈的主动维护。
In other words, some embodiments of the present invention provide for proactive maintenance of gradient coils, as opposed reactively detecting an already failed gradient coil.
在此类实施方案中，通过降低患者将暴露于过度的梯度线圈噪声/振动的风险，对梯度线圈的主动维护可以提高患者安全性/舒适度。
In such embodiments, proactive maintenance of gradient coils may improve patient safety/comfort by reducing the risk that a patient will be exposed to excessive gradient coil noise/vibrations.
此外，通过使用MRI系统的梯度放大器来获得反EMF参数读数，本发明的一些实施方案提供了一种监测梯度线圈的健康状态的系统，该系统利用了当前存在于许多MRI系统中的现有传感器/设备。
Further still, by using the gradient amplifiers of an MRI system to obtain back EMF parameter readings, some embodiments of the present invention provide for a system of monitoring the health status of a gradient coil that makes use of existing sensors/equipment presently found in many MRI systems.
因此，本发明的此类实施方案提供了对梯度线圈的健康状态的改进的监测，而不会产生通常与在各种位置已经使用的现有MRI系统中开发和/或安装新型传感器相关的大量费用。
Thus, such embodiments of the present invention provide for improved monitoring of the health status of a gradient coil without incurring the significant costs typically associated with developing and/or installing new types of sensors into existing MRI systems already in use at various locations.
更进一步，通过在服务器中存储和分析多个MRI系统中的梯度线圈的历史数据集/参数读数，本发明的一些实施方案可以提供对未来的梯度线圈失效的改进的理解。
Yet further still, by storing and analyzing historical dataset/parameter readings, of gradient coils in multiple MRI systems, in a server, some embodiments of the invention may provide for improved understanding of future gradient coil failures.
例如，在此类实施方案中，通过神经网络对失效梯度线圈的加速历史的历史数据的分析可以有助于更快地识别失效的根本原因。
For example, in such embodiments, analysis of the historical data of the acceleration history of a failed gradient coil by the neural network may facilitate faster identification of the root cause of the failure.
除了任何先前指示的修改之外，本领域技术人员可以在不脱离本发明的实质和范围的情况下设计出许多其他变型和另选布置，并且所附权利要求书旨在覆盖此类修改和布置。
In addition to any previously indicated modification, numerous other variations and alternative arrangements may be devised by those skilled in the art without departing from the spirit and scope of this invention, and the appended claims are intended to cover such modifications and arrangements.
因此，尽管上面已经结合当前被认为是最实际和最优选的方面对本发明进行了具体和详细的描述，但对于本领域的普通技术人员将显而易见的是，在不脱离本文阐述的原理和概念的情况下，可以进行许多修改，包括但不限于形式、功能、操作方式和/或使用。
Thus, while the invention has been described above with particularity and detail in connection with what is presently deemed to be the most practical and preferred aspects, it will be apparent to those of ordinary skill in the art that numerous modifications, including, but not limited to, form, function, manner of operations, and/or use may be made without departing from the principles and concepts set forth herein.
最后，本文所用的示例和实施方案仅意图是说明性的，并且不应以任何方式解释为限制性的。
Finally, the examples and embodiments used herein are meant to be illustrative only and should not be construed to be limiting in any manner.
在穿过井期间，测量单元200在穿过井到测量位置308的同时，测量单元200可以用于连续性或周期性地获取温度测量值、压力测量值或它们的任意组合。
During traverse of the well, the measurement unit 200 may be used to continuously or periodically acquire temperature measurements, pressure measurements, or any combination thereof while traversing the well to the measurement location 308.
可以理解的是，可以根据来自先前执行的测井操作的测井记录以及基于测井停止在相等基础上的生产分段来确定测量位置308。
As will be appreciated, the measurement location 308 may be determined from logs from previously performed logging operations, as well segmentation of production on an equal basis based on log stops.
在到达测量位置308时，无人潜航器100可以停止移动并保持静止(即，不使用推进单元106)一个时间段以获取井段302中流体(其流动由箭头310描述)的测量值。
Upon reaching the measurement location 308, the unmanned submersible vehicle 100 may stop moving and remain stationary (that is, without using the propulsion units 106) for a time period to acquire measurements of a fluid (the flow of which is depicted by arrows 310) in the well section 302.
例如流体可以是水、油、气体或它们的任何组合。
The fluid may be, for example, water, oil, gas, or any combination thereof.
在测量位置308处，无人潜航器100可以使用两个推进单元来测量流体310的流速。
At the measurement location 308, the unmanned submersible vehicle 100 may measure the flow velocity of the fluid 310 using two of the propulsion units.
无人潜航器100可以将两个推进单元枢转到流体流(依赖于井的水平到竖直(H/V)结构)中，从而可以根据已知技术根据流体流中的推进器的旋转来确定流速的测量值。
The unmanned submersible vehicle 100 may pivot two of the propulsion units into the fluid flow (relying on the horizontal to vertical (WV) structure of the well), such that the measurement of the flow velocity may be determined according to the rotation of the propellers in the fluid flow according to known techniques.
另外，在一些实施例中，用于测量流速的两个推进器的旋转可以提供电能，以通过与每个推进器相联接的发电机来为动力单元208的电池充电。
Additionally, the rotation of the two propellers used to measure flow velocity may, in some embodiments, provide electrical energy to charge a battery of the power unit 208 via a generator coupled to each propeller.
无人潜航器100可以在测量位置308处获取附加测量值。
The unmanned submersible vehicle 100 may acquire additional measurements at the measurement location 308.
例如，除了在穿过井到测量位置308期间连续地或周期性地获取的那些测量值之外，测量单元200还可以用于获取温度测量值、压力测量值、梯度测量值或它们的任意组合。
For example, the measurement unit 200 may be used to acquire temperature measurements, pressure measurements, gradient measurements, or any combination thereof, in addition to those measurement continuously or periodically acquired during traversal of the well to the measurement location 308.
在测量位置308处获取测量值之后，无人潜航器可以前进至另一个测量位置或者离开井。
After acquisition of measurements at the measurement location 308, the unmanned submersible vehicle may proceed to another measurement location or exit the well.
例如，存在附加的测量位置，无人潜航器可以远程或自主移动到下一个测量位置。
For example, additional measurement locations exist, the unmanned submersible vehicle may be remotely or autonomously moved to the next measurement location.
如果不存在其他测量位置，则无人潜航器100可以离开井。
If no other measurement locations exist, the unmanned submersible vehicle 100 may exit the well.
在这种情况下，无人潜航器可以远程或自主地移动到能够通过流出井的流体离开井的井段。
In such instances, the unmanned submersible vehicle may be remotely or autonomously moved to a section of the well that enables exiting of the well via the flow out of the well.
在一些实施例中，无人潜航器可以使用推进单元106来辅助离开井(例如，如果流体流不足以将无人潜航器100从井中移出)。
In some embodiments, the unmanned submersible vehicle may use the propulsion units 106 to assist in exiting the well (for example, if the fluid flow is insufficient to move the unmanned submersible vehicle 100 out of the well).
图4是根据本公开实施例的使用本文中所描述的无人潜航器来勘测井的过程400的框图。
FIG. 4 is a block diagram of a process 400 for surveying a well using the unmanned submersible vehicle described herein in accordance with an embodiment of the disclosure.
初始，无人潜航器可以进行启动程序(步骤402)。
Initially, an unmanned submersible vehicle may undergo a startup sequence (block 402).
例如，可以初始化诸如测量单元、定位单元、相机等电气组件以确保适当操作。
For example, the startup may include powering on the unmanned submersible vehicle, initializing electronic components of the unmanned submersible vehicle, etc. For example, electric components such as the measurement unit, location unit, camera, and so on may be initialized to ensure proper operation.
接下来，可以将无人潜航器插入到井中(步骤404)。
Next the unmanned submersible vehicle may be inserted into a well (block 404).
在一些实施例中，在插入无人潜航器器期间，可以关闭井。
In some embodiments, the well may be shut-in during insertion of the unmanned submersible vehicle.
然后，在通过无人潜航器勘测期间，井可以保持关闭，或者可以进行生产。
The well may then remain shut-in during surveying by the unmanned submersible vehicle or may be in production.
在插入到井中之后，无人潜航器可以通过重力移动到井眼的最低段(步骤406)。
After insertion into the well, the unmanned submersible vehicle may move via gravity to the lowest section of the wellbore (block 406).
例如，定位单元、测量单元或两者可以用于判断无人潜航器何时位于井的最低段。
For example, the location unit, the measurement unit, or both may be used to determine when the unmanned submersible vehicle is located at the lowest section of the well.
在到达井的最低段之后，无人潜航器在获取测量值的同时穿过井到测量位置(步骤408)。
After reaching the lowest section of the well, the unmanned submersible vehicle traverse the well to a measurement location while acquiring measurements (block 408).
例如，无人潜航器在移动通过井的同时可以连续地或周期性地获取温度、压力和梯度测量值。
For example, the unmanned submersible vehicle may continuously or periodically acquire temperature, pressure, and gradient measurements while moving through a well.
测量位置可以在井的生产段中，使得无人潜航器从井中的初始位置移动至生产段。
The measurement location may be in a production section of the well, such that the unmanned submersible vehicle moves from the initial location in a well to a production section.
在到达测量位置后，无人潜航器可以停止推进(即，通过停止对推进单元的供电)并且在测量位置处获取测量值(步骤410)。
After reaching the measurement location, the unmanned submersible vehicle may stop propulsion (that is, by ceasing powering of the propulsion units) and acquire measurements at the measurement location (block 410).
例如，如本公开中所讨论的，无人潜航器可以使用推进单元的推进器在测量位置处测量流体的流速。
For example, as discussed in the disclosure, the unmanned submersible vehicle may measure the flow velocity of a fluid at the measurement location using the propellers of the propulsion units.