MX2016009091A - Construccion y uso de base de modelo de fluido optico. - Google Patents
Construccion y uso de base de modelo de fluido optico.Info
- Publication number
- MX2016009091A MX2016009091A MX2016009091A MX2016009091A MX2016009091A MX 2016009091 A MX2016009091 A MX 2016009091A MX 2016009091 A MX2016009091 A MX 2016009091A MX 2016009091 A MX2016009091 A MX 2016009091A MX 2016009091 A MX2016009091 A MX 2016009091A
- Authority
- MX
- Mexico
- Prior art keywords
- model base
- sensor responses
- base construction
- fluid model
- optical fluid
- Prior art date
Links
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/10—Locating fluid leaks, intrusions or movements
- E21B47/113—Locating fluid leaks, intrusions or movements using electrical indications; using light radiations
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/003—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells by analysing drilling variables or conditions
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
- E21B49/087—Well testing, e.g. testing for reservoir productivity or formation parameters
- E21B49/0875—Well testing, e.g. testing for reservoir productivity or formation parameters determining specific fluid parameters
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mining & Mineral Resources (AREA)
- Geology (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Geochemistry & Mineralogy (AREA)
- Evolutionary Computation (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Fluid Mechanics (AREA)
- Environmental & Geological Engineering (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Geophysics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
Los aparatos, sistemas y métodos pueden funcionar para seleccionar un subconjunto de respuestas de sensores como entradas para cada uno de los múltiples modelos precalibrados para predecir cada una de las múltiples propiedades de los fluidos de la formación. Las respuestas de sensores se obtienen y se vuelven a procesar de una herramienta de medición de fondo de pozo. Cada una de las múltiples propiedades de los fluidos de la formación previstas se evalúa mediante la aplicación de restricciones en las concentraciones de hidrocarburos, la geofísica y/o la petrofísica. La selección de respuestas de sensores y los modelos asociados de una base de modelo preconstruido o un grupo de candidatos se ajustan y se vuelven a procesar para validar la selección de modelos.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/US2014/013192 WO2015112177A1 (en) | 2014-01-27 | 2014-01-27 | Optical fluid model base construction and use |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| MX2016009091A true MX2016009091A (es) | 2016-10-13 |
Family
ID=53681807
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| MX2016009091A MX2016009091A (es) | 2014-01-27 | 2014-01-27 | Construccion y uso de base de modelo de fluido optico. |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US9702248B2 (es) |
| EP (1) | EP2925964A4 (es) |
| BR (1) | BR112016013211B1 (es) |
| MX (1) | MX2016009091A (es) |
| SA (1) | SA516371311B1 (es) |
| WO (1) | WO2015112177A1 (es) |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR112016013211B1 (pt) | 2014-01-27 | 2021-12-07 | Halliburton Energy Services, Inc | Método para identificar propriedades de fluido de formação e aparelho |
| BR112017006099A2 (pt) * | 2014-12-04 | 2018-01-30 | Halliburton Energy Services Inc | sistema, método, e, meio legível por computador para caracterização de fluidos presentes em furos de poços de hidrocarbonetos. |
| WO2016204752A1 (en) * | 2015-06-17 | 2016-12-22 | Landmark Graphics Corporation | Automated pvt characterization and flow metering |
| US10108754B2 (en) | 2015-10-30 | 2018-10-23 | Halliburton Energy Services, Inc. | Method for ruggedizing integrated computational elements for analyte detection in the oil and gas industry |
| US10725203B2 (en) * | 2015-11-18 | 2020-07-28 | Halliburton Energy Services, Inc. | Dual-sensor tool optical data processing through master sensor standardization |
| US10519770B2 (en) | 2016-03-22 | 2019-12-31 | Halliburton Energy Services, Inc. | Calibration module for pooled optical sensors in downhole fluid analysis |
| US20180284755A1 (en) | 2016-05-09 | 2018-10-04 | StrongForce IoT Portfolio 2016, LLC | Methods and systems for data storage in an industrial internet of things data collection environment with large data sets |
| US10983507B2 (en) | 2016-05-09 | 2021-04-20 | Strong Force Iot Portfolio 2016, Llc | Method for data collection and frequency analysis with self-organization functionality |
| US11774944B2 (en) | 2016-05-09 | 2023-10-03 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for the industrial internet of things |
| US11327475B2 (en) | 2016-05-09 | 2022-05-10 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for intelligent collection and analysis of vehicle data |
| US11237546B2 (en) | 2016-06-15 | 2022-02-01 | Strong Force loT Portfolio 2016, LLC | Method and system of modifying a data collection trajectory for vehicles |
| BR112019004026A2 (pt) * | 2016-09-20 | 2019-05-28 | Halliburton Energy Services Inc | método, ferramenta de análise de fluidos e mídia de armazenamento não transitória legível por computador |
| WO2018056976A1 (en) | 2016-09-22 | 2018-03-29 | Halliburton Energy Services, Inc. | Methods and systems for obtaining high-resolution spectral data of formation fluids from optical computing device measurements |
| US20190120049A1 (en) * | 2016-11-04 | 2019-04-25 | Halliburton Energy Services, Inc. | Universal Downhole Fluid Analyzer With Generic Inputs |
| US11131989B2 (en) | 2017-08-02 | 2021-09-28 | Strong Force Iot Portfolio 2016, Llc | Systems and methods for data collection including pattern recognition |
| CA3072045A1 (en) | 2017-08-02 | 2019-02-07 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for detection in an industrial internet of things data collection environment with large data sets |
| US10914677B2 (en) | 2018-04-24 | 2021-02-09 | General Electric Company | System and method for calibrating a melt pool monitoring system of an additive manufacturing machine |
| US11467314B2 (en) * | 2018-07-16 | 2022-10-11 | Halliburton Energy Services, Inc. | Optical sensor adaptive calibration |
| JP7545977B2 (ja) * | 2018-09-08 | 2024-09-05 | アルプビジョン、ソシエテアノニム | バイオロジカルニューラルネットワークに基づく認知コンピューティングの方法とシステム |
| US10962507B2 (en) | 2018-11-28 | 2021-03-30 | General Electric Company | System and method for calibrating an acoustic monitoring system of an additive manufacturing machine |
| US11604982B2 (en) | 2019-10-10 | 2023-03-14 | Halliburton Energy Services, Inc. | Progressive modeling of optical sensor data transformation neural networks for downhole fluid analysis |
| US11459881B2 (en) * | 2020-05-26 | 2022-10-04 | Halliburton Energy Services, Inc. | Optical signal based reservoir characterization systems and methods |
| US12309327B2 (en) * | 2021-02-24 | 2025-05-20 | General Electric Company | Automated beam scan calibration, alignment, and adjustment |
| WO2023196389A1 (en) * | 2022-04-05 | 2023-10-12 | Schlumberger Technology Corporation | Determination of asphaltene onset condition of reservoir fluids during downhole fluid analysis |
| WO2024118790A1 (en) * | 2022-11-29 | 2024-06-06 | Schlumberger Technology Corporation | Prediction of fluid density based on optical absorption measurements |
| US20260009725A1 (en) * | 2022-12-13 | 2026-01-08 | Schlumberger Technology Corporation | Systems and methods for determining carbon dioxide concentrations using peak ratio-based optical spectrometric measurements |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6178815B1 (en) * | 1998-07-30 | 2001-01-30 | Schlumberger Technology Corporation | Method to improve the quality of a formation fluid sample |
| US6441388B1 (en) * | 1998-10-13 | 2002-08-27 | Rio Grande Medical Technologies, Inc. | Methods and apparatus for spectroscopic calibration model transfer |
| US7081615B2 (en) * | 2002-12-03 | 2006-07-25 | Schlumberger Technology Corporation | Methods and apparatus for the downhole characterization of formation fluids |
| US6995360B2 (en) * | 2003-05-23 | 2006-02-07 | Schlumberger Technology Corporation | Method and sensor for monitoring gas in a downhole environment |
| RU2266523C1 (ru) | 2004-07-27 | 2005-12-20 | Общество с ограниченной ответственностью ООО "ВИНТЕЛ" | Способ создания независимых многомерных градуировочных моделей |
| US7490664B2 (en) * | 2004-11-12 | 2009-02-17 | Halliburton Energy Services, Inc. | Drilling, perforating and formation analysis |
| US7711488B2 (en) * | 2006-12-28 | 2010-05-04 | Schlumberger Technology Corporation | Methods and apparatus to monitor contamination levels in a formation fluid |
| EP2191103A1 (en) * | 2007-08-20 | 2010-06-02 | Halliburton Energy Service, Inc. | Apparatus and method for fluid property measurements |
| US8434357B2 (en) | 2009-08-18 | 2013-05-07 | Schlumberger Technology Corporation | Clean fluid sample for downhole measurements |
| BR122020014484B1 (pt) | 2010-12-08 | 2022-03-22 | Halliburton Energy Services, Inc | Método para determinar uma propriedade desconhecida de um fluido de reservatório |
| US9638681B2 (en) | 2011-09-30 | 2017-05-02 | Schlumberger Technology Corporation | Real-time compositional analysis of hydrocarbon based fluid samples |
| CA2858591A1 (en) * | 2011-12-16 | 2013-06-20 | Halliburton Energy Services, Inc. | Methods of calibration transfer for a testing instrument |
| AU2013380988B2 (en) | 2013-03-08 | 2016-09-22 | Halliburton Energy Services, Inc | Systems and methods for optical fluid identification approximation and calibration |
| BR112016013211B1 (pt) | 2014-01-27 | 2021-12-07 | Halliburton Energy Services, Inc | Método para identificar propriedades de fluido de formação e aparelho |
-
2014
- 2014-01-27 BR BR112016013211-4A patent/BR112016013211B1/pt active IP Right Grant
- 2014-01-27 MX MX2016009091A patent/MX2016009091A/es unknown
- 2014-01-27 EP EP14816091.4A patent/EP2925964A4/en not_active Withdrawn
- 2014-01-27 US US14/436,017 patent/US9702248B2/en active Active
- 2014-01-27 WO PCT/US2014/013192 patent/WO2015112177A1/en not_active Ceased
-
2016
- 2016-06-13 SA SA516371311A patent/SA516371311B1/ar unknown
Also Published As
| Publication number | Publication date |
|---|---|
| SA516371311B1 (ar) | 2021-09-08 |
| BR112016013211A2 (pt) | 2017-08-08 |
| WO2015112177A1 (en) | 2015-07-30 |
| EP2925964A4 (en) | 2016-07-13 |
| US20160273354A1 (en) | 2016-09-22 |
| BR112016013211B1 (pt) | 2021-12-07 |
| US9702248B2 (en) | 2017-07-11 |
| EP2925964A1 (en) | 2015-10-07 |
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