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1. 发明申请

WO2011115747A3 METHOD AND APPARATUS FOR NEUTRON POROSITY MEASUREMENT USING A NEURAL NETWORK 审中-公开
标题翻译：用神经网络测量中子多孔性的方法和装置
公开(公告)号：WO2011115747A3
公开(公告)日：2011-12-22
申请号：PCT/US2011026783
申请日：2011-03-02
申请人： BAKER HUGHES INC , ZHANG QIANMEI , CHALITSIOS CONSTANTYN
发明人： ZHANG QIANMEI , CHALITSIOS CONSTANTYN
IPC分类号： G01V5/10 , G06N3/02
CPC分类号： G01V5/125 , G01V5/04 , Y10S706/929
摘要： A method of estimating formation porosity using a neural network for neutron porosity tools. In the training stage, the near-to-far ratio, environmental variables, such as mineralogy, borehole size, standoff etc., are fed to the inputs and the neural network is trained for obtaining the related true porosity (the output). The trained neural network is implanted into tool's firmware for the real time porosity measurement, accounting for the environmental effects considered during training.
摘要翻译：一种使用中子孔隙度工具的神经网络估算地层孔隙度的方法。在训练阶段，将近 - 远比例的环境变量（如矿物学，井眼大小，间距等）输入到输入中，并对神经网络进行训练以获得相关的真实孔隙度（输出）。经过训练的神经网络被植入到工具的固件中，用于实时孔隙度测量，以考虑在训练期间考虑的环境影响。

2. 发明申请

WO00016126A1 SYSTEM AND METHOD FOR DELINEATING SPATIALLY DEPENDENT OBJECTS, SUCH AS HYDROCARBON ACCUMULATIONS FROM SEISMIC DATA 审中-公开
标题翻译：用于分类空间依赖对象的系统和方法，如地震数据中的烃类累积
公开(公告)号：WO00016126A1
公开(公告)日：2000-03-23
申请号：PCT/US1999/019913
申请日：1999-08-31
IPC分类号： G01V1/30 , G01V3/38 , G01V11/00
CPC分类号： G01V1/30 , G01V3/38 , Y10S706/929
摘要： A neural network based system, method, and process for the automated delineation of spatially dependent objects is disclosed. The method is applicable to objects such as hydrocarbon accumulations, aeromagnetic profiles, astronomical clusters, weather clusters, objects from radar, sonar, seismic and infrared returns, etc. One of the novelties in the present invention is that the method can be utilized whether or not known data is available to provide traditional training sets. The output consists of a classification of the input data into clearly delineated accumulations, clusters, objects, etc. that have various types and properties. A preferred but non-exclusive application of the present invention is the automated delineation of hydrocarbon accumulations and sub-regions within the accumulations with various properties, in an oil and gas field, prior to the commencement of drilling operations.
摘要翻译：公开了一种基于神经网络的系统，方法和用于自动描绘空间依赖对象的过程。该方法适用于碳氢化合物积累，航磁剖面，天文群，天气群，雷达物体，声纳，地震和红外线回报等物体。本发明的新颖之处在于可以利用该方法，未知的数据可用于提供传统的训练集。输出包括将输入数据分类为具有各种类型和属性的清晰描绘的积累，群集，对象等。本发明的优选但不排他的应用是在钻井操作开始之前在石油和天然气领域中具有各种性质的积聚物中的碳氢化合物积聚物和亚区域的自动描绘。

3. 发明申请

WO2014070572A3 ROCK FACIES PREDICTION IN NON-CORED WELLS FROM CORED WELLS 审中-公开
标题翻译：岩石类岩石中非晶核的岩石预测
公开(公告)号：WO2014070572A3
公开(公告)日：2014-08-28
申请号：PCT/US2013066561
申请日：2013-10-24
申请人： SAUDI ARABIAN OIL CO , ARAMCO SERVICES CO
发明人： SUNG ROGER R , LI YUNSHENG , SUN CHUANYU STEPHEN
IPC分类号： G01V11/00 , G01V99/00
CPC分类号： G01V11/00 , E21B2041/0028 , G01V99/005 , Y10S706/929
摘要： Facies in wells in areas of a hydrocarbon reservoir are predicted or postulated. Artificial neural networks are utilized to build a training image based on rock phases which are described and interpreted using existing data obtained from certain wells in the reservoir, and also well log characteristics of those same wells for each rock facies. Well logs from which wells where no well core data has been collected are then analyzed against the training image and the rock facies in the non-cored wells are postulated. The cost and also the possibility of damage to the wells from extraction of the core rock during drilling are avoided.
摘要翻译：预测或假设在油气藏地区的井中的相。人工神经网络被用于建立基于岩相的训练图像，这些岩相使用从储层中的某些井获得的现有数据进行描述和解释，以及每个岩相相同井的井测井特征。然后根据训练图像分析未收集核心数据的井的测井记录，并假定非核心井中的岩相。避免了在钻井过程中从岩心中提取钻井造成的损失以及损坏井的可能性。

4. 发明申请

WO2014070572A2 ROCK FACIES PREDICTION IN NON-CORED WELLS FROM CORED WELLS 审中-公开
公开(公告)号：WO2014070572A2
公开(公告)日：2014-05-08
申请号：PCT/US2013066561
申请日：2013-10-24
申请人： SAUDI ARABIAN OIL OCMPANY , ARAMCO SERVICES CO
发明人： SUNG ROGER R , LI YUNSHENG , SUN CHUANYU STEPHEN
IPC分类号： G01V11/00
CPC分类号： G01V11/00 , E21B2041/0028 , G01V99/005 , Y10S706/929
摘要： Facies in wells in areas of a hydrocarbon reservoir are predicted or postulated. Artificial neural networks are utilized to build a training image based on rock phases which are described and interpreted using existing data obtained from certain wells in the reservoir, and also well log characteristics of those same wells for each rock facies. Well logs from which wells where no well core data has been collected are then analyzed against the training image and the rock facies in the non-cored wells are postulated. The cost and also the possibility of damage to the wells from extraction of the core rock during drilling are avoided.

5. 发明申请

WO2011115747A2 METHOD AND APPARATUS FOR NEUTRON POROSITY MEASUREMENT USING A NEURAL NETWORK 审中-公开
标题翻译：使用神经网络进行中子孔隙度测量的方法和装置
公开(公告)号：WO2011115747A2
公开(公告)日：2011-09-22
申请号：PCT/US2011/026783
申请日：2011-03-02
申请人： BAKER HUGHES INCORPORATED , ZHANG, Qianmei , CHALITSIOS, Constantyn
发明人： ZHANG, Qianmei , CHALITSIOS, Constantyn
CPC分类号： G01V5/125 , G01V5/04 , Y10S706/929
摘要： A method of estimating formation porosity using a neural network for neutron porosity tools. In the training stage, the near-to-far ratio, environmental variables, such as mineralogy, borehole size, standoff etc., are fed to the inputs and the neural network is trained for obtaining the related true porosity (the output). The trained neural network is implanted into tool's firmware for the real time porosity measurement, accounting for the environmental effects considered during training.
摘要翻译：一种使用中子孔隙度工具的神经网络估算地层孔隙度的方法。在训练阶段，近距离比例，诸如矿物学，井眼尺寸，对等等的环境变量被馈送到输入，并且训练神经网络以获得相关的真实孔隙度（输出）。将训练有素的神经网络植入工具固件中进行实时孔隙度测量，考虑到训练期间考虑的环境影响。

6. 发明申请

WO99060424A1 SEISMIC SURVEY SYSTEM 审中-公开
标题翻译：地震调查系统
公开(公告)号：WO99060424A1
公开(公告)日：1999-11-25
申请号：PCT/US1999/010722
申请日：1999-05-14
IPC分类号： G01V1/20 , G01V1/22 , G01V1/38 , G01V1/26
CPC分类号： G01V1/22 , G01V1/20 , Y10S706/928 , Y10S706/929
摘要： A wireless seismic survey (220) system is structured as a hierarchy of cell (225) network layers, each higher level cell network layer (15) covering a bigger area than a lower level cell network layer (20) and receiving data from the lower level cell network layer. This hierarchical structure functions to concentrate collected data to a level that can be communicated to a central collection point, either through wireless, cable or other media. By defining cells to cover predetermined areas, transmission frequencies and/or transmission time slots or codes can be reused by cells sufficiently spaced apart to avoid co-channel interference, thereby increasing the amount of usable bandwidth.
摘要翻译：无线地震勘测（220）系统被构造为单元（225）网络层的层次结构，每个较高层小区网络层（15）覆盖比较低级小区网络层（20）更大的区域，并从下层接收数据级单元网络层。这种分层结构用于将收集的数据集中到通过无线，有线或其他媒体传送到中央收集点的级别。通过定义单元以覆盖预定区域，传输频率和/或传输时隙或代码可以被足够间隔开的小区重用，以避免同信道干扰，从而增加可用带宽的数量。

7. 发明申请

WO1997011392A1 AUTOMATIC SEISMIC PATTERN RECOGNITION METHOD 审中-公开
标题翻译：自动地震图像识别方法
公开(公告)号：WO1997011392A1
公开(公告)日：1997-03-27
申请号：PCT/FR1996001396
申请日：1996-09-11
申请人： ELF AQUITAINE PRODUCTION , KESKES, Naamen
发明人： ELF AQUITAINE PRODUCTION
IPC分类号： G01V01/30
CPC分类号： G01V1/301 , Y10S706/929
摘要： An automatic seismic pattern recognition method is characterised in that it includes determining a given number of seismic patterns to be recognised; providing a set of seismic trace portions for said region; defining a pattern recognition parameter common to all the trace portions, and determining the value of said parameter for each of the trace portions of the set; selecting trace portions among said set; selecting a one-dimensional neural network containing as many cells as there are patterns to be recognised, each cell being assigned a value of the recognition parameter; submitting the neural network to a learning process with the selected trace portions, so that at the end of said process, each cell matches a pattern to be recognised, and that said patterns are progressively ordered; presenting each trace portion of said set to be processed to the classified and ordered neural network; and attributing to each trace portion presented to the network the number of the cell closest to it. The invention is particularly useful for recognising seismic patterns between two horizons defined by plotting a seismic section.
摘要翻译：一种自动地震模式识别方法，其特征在于它包括确定要识别的给定数量的地震模式; 为所述区域提供一组地震迹线部分; 定义所有跟踪部分共同的模式识别参数，以及确定所述组的每个跟踪部分的所述参数的值; 在所述组中选择轨迹部分; 选择包含与要识别的模式一样多的单元的一维神经网络，每个单元被分配识别参数的值; 将所述神经网络提交到具有所选择的跟踪部分的学习过程，使得在所述处理结束时，每个小区匹配要被识别的模式，并且所述模式被逐行排序; 将要处理的所述组的每个跟踪部分呈现给分类和排序的神经网络; 并且将呈现给网络的每个跟踪部分归因于最接近它的单元的数量。本发明对于识别通过绘制地震剖面而定义的两个视野之间的地震模式特别有用。

8. 发明申请

WO1994003827A1 METHOD AND APPARATUS FOR PRODUCING A POROSITY LOG OF A SUBSURFACE FORMATION 审中-公开
标题翻译：用于生成表面形成的孔隙度记录的方法和装置
公开(公告)号：WO1994003827A1
公开(公告)日：1994-02-17
申请号：PCT/US1993007490
申请日：1993-08-09
申请人： MOBIL OIL CORPORATION
发明人： MOBIL OIL CORPORATION , ALLEN, Linus, Scott , MILLS, William, Raymond
IPC分类号： G01V05/10
CPC分类号： G01V5/107 , Y10S706/928 , Y10S706/929
摘要： A borehole logging tool (10) is lowered into a borehole traversing a subsurface formation and a neutron detector (19) measures the die-away of nuclear radiation in the formation. Intensity signals are produced representing the die-away of nuclear radiation as the logging tool (10) traverses the borehole. A signal processor (17), employing as least one neural network, processes the intensity signals and produces a standoff-corrected epithermal neutron lifetime signal to correct for standoff from the borehole wall (24) encountered by the detector (19) as the logging tool (10) traverses the borehole. The signal processor further generates a porosity signal from the standoff-corrected epithermal neutron lifetime signal derived from measurements in borehole models at known porosities and conditions of detector (19) standoff. A log is generated of such porosity signal versus depth as the logging tool (10) traverses the borehole.
摘要翻译：井眼测井工具（10）被下降到穿过地下地层的钻孔中，并且中子探测器（19）测量地层中核辐射的死亡。当测井工具（10）穿过井眼时，产生强度信号代表核辐射的消失。使用至少一个神经网络的信号处理器（17）处理强度信号并产生间隔校正的超热中子寿命信号，以校正由检测器（19）遇到的钻孔壁（24）的间隔作为测井工具（10）穿过井眼。信号处理器还从已知的孔隙度和检测器（19）间隔的条件下从井眼模型中的测量得到的间隔校正的超热中子寿命信号中产生孔隙度信号。当测井工具（10）穿过井眼时，产生这样的孔隙度信号对深度的对数。

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