Oil field operators dedicate significant resources to improve the recovery of hydrocarbons from reservoirs while reducing recovery costs. To achieve these goals, reservoir engineers both monitor the current state of the reservoir and attempt to predict future behavior given a set of current and/or postulated conditions. Reservoir monitoring, sometimes referred to as reservoir surveillance, involves the regular collection and monitoring of measured near-wellbore production data from within and around the wells of a reservoir. Such data may be collected using sensors installed inline along production tubing introduced into the well. The data may include, but is not limited to, water saturation, water and oil cuts, fluid pressure and fluid flow rates, is generally collected at a fixed, regular interval (e.g., once per minute) and is monitored in real-time by field personnel. As the data is collected, it is archived into a historical database.
The production and survey data is incorporated into simulations that are executed as part of the well surveillance and model the behavior of the entire reservoir. Such simulations predict the current overall state, producing simulated interwell data values both near and at a distance from the wellbore. Simulated near-wellbore interwell data is regularly correlated against measured near-wellbore data, with modeling parameters being adjusted as needed to reduce the error between simulated and measured data. Once so adjusted, the simulated interwell data, both near and at a distance from the wellbore, may be relied upon to assess the overall state of the reservoir.
The collected production data, however, mostly reflects conditions immediately around the reservoir wells. To provide a more complete picture of the state of the reservoir, periodic surveys of the reservoir are performed. Such surveys can include large scale electromagnetic (EM) surveys that may be performed months or even years apart. The surveys can subsequently be combined to provide a time-lapse image of a reservoir to identify trends and adjust production strategies to optimize the production of the reservoir.
However, as a result of the large periods of time between surveys, the use of permanently deployed sensors (i.e., sensors expected to be deployed once and operated for the predicted lifespan of a reservoir) has largely been considered impractical. This is because the environments to which EM sensors are exposed are generally too harsh to operate existing sensors reliably over such long periods and too inaccessible to perform any sort of equipment maintenance, failure diagnosis or repair in a cost effective manner, if at all. This is true of both offshore and onshore environments. Offshore reservoirs may be located at significant depths where the pressure and salinity can take its toll on equipment and accessibility may be limited to remotely operated vehicles with limited capabilities and high deployment and operations costs. Onshore reservoirs may appear to be more accessible, but because onshore sensors and their corresponding communication and/or power networks must be buried underground, the cost of equipment maintenance, failure diagnosis and repair can still be substantial and in some cases prohibitive. Even absent overt failures, these hostile environments can still produce long-term shifts in the measurements taken by the sensors that render the measurements inconsistent as between surveys and preclude any meaningful correlation of the survey data.
Accordingly, there are disclosed herein methods and systems for electromagnetic (EM) reservoir monitoring. In the drawings:
It should be understood that the drawings and corresponding detailed description do not limit the disclosure, but on the contrary, they provide the foundation for understanding all modifications, equivalents, and alternatives falling within the scope of the appended claims.
The paragraphs that follow describe illustrative systems and methods for electromagnetic (EM) reservoir monitoring. Illustrative production environments are first described, followed by a description of various illustrative reservoir surveillance sensor array configurations suitable for use with the disclosed systems and methods. Illustrative sensors usable within the disclosed arrays are then disclosed, together with examples of various optical transducers suitable for incorporation into the disclosed sensors. Finally, an illustrative EM reservoir monitoring method is presented within the context of an illustrative system and a software-based implementation by said system.
The disclosed systems and methods are best understood in the context of the larger systems in which they operate. Accordingly,
The platform also houses a processing and control system 750 which couples to a surface interface module 710. Surface interface module 710 in turn couples to subsea interface module 730 through umbilical 104, providing power from the surface to the equipment on the sea bottom. It should be noted that although described in terms of a sea or ocean, the systems and methods described herein are suitable for use within a variety of bodies of water such as, for example, a lake, a bay or a transition zone. Subsea interface module 730 couples to an array of EM sensors 300 through optical fiber 302. In other illustrative embodiments, a tubing encapsulated conductor (TEC) may be used instead of an optical fiber. A TEC can include multiple fibers and conductors, allowing a wide variety of sensors and subsea equipment to be interconnected through a single trunk cable capable of enduring the harsh conditions at the sea bottom.
Each EM sensor 300 reacts to EM signals induced into the formation below seabed 112A by either natural EM sources or one or more controlled EM sources housed within subsea interface module 730, though in other embodiments the controlled EM source(s) could be located elsewhere, for example in a separate housing on seabed 112A or within the casing of a completed reservoir well. In at least some illustrative embodiments, a light source within subsea interface module 730 directs light through optical fiber 302 and then triggers a controlled EM source, which induces EM signals within formation 114. These EM signals are received by one or more EM sensors 300, causing the sensors to modulate the interrogation light beam. Each sensor's modulation maybe distinguished by any of a number of known techniques such as time multiplexing or frequency multiplexing. The modulated beam is directed back to a detector within subsea interface module 730, where the modulation is encoded and sent to the surface for further processing. The processed data may then be used to assess the resistivity of the various formation layers, which in turn may be used to characterize the composition of the formation (e.g., using any of a number of known inversion techniques) and to identify the current state of subsea reservoir 110A.
A similar configuration for onshore reservoir monitoring is shown in
The embodiments of both
Other sensor array and optical fiber configurations are also suitable for use with the disclosed systems and methods. For example, in
The arrangements of
Many illustrative sensor configurations suitable for use in the disclosed embodiments may be implemented. For example, multiple sensors may be coupled in series on each branch of the
The use of the above-described sensor arrays provides the capability of monitoring the state of reservoirs covering larger geographic expanses, whether onshore or offshore. Nonetheless, in order to provide long-term data that can be used to assess changes as hydrocarbons are extracted from a reservoir, it is desirable to perform multiple surveys of the reservoir, such as EM surveys, separated by one or more months, and in some instances separated by one or more years. Such data can be useful in identifying and predicting long-term reductions in the productivity of the reservoir. In at least some illustrative embodiments, data acquired over such long periods of time is used to build a time lapse reservoir model that can be used to predict the long-term dynamic behavior of the reservoir. Such predictions enable reservoir engineers to make changes in production elements such as, for example, the location and configuration of injector wells over time and thus optimize the overall production of the reservoir over its expected lifespan.
In at least some illustrative embodiments, the sensor 300 configuration shown in
In the example embodiment of
A number of different optical transducers that convert the electric potential produced across sensing surfaces 308 and 310 of illustrative sensor 300 into a modulation of the interrogation light beam transmitted through optical fiber 302 may be suitable for incorporation into sensor 300.
Beams 406a are formed from a resilient, flexible material that enables an electric or magnetic field (depending on the compositions of field sensitive element 402) to displace the movable layer 404b along a translational axis parallel to the beam of light (as indicated by the upward and downward facing arrows). In the embodiment shown, the beams 406a provide a restoring force that returns the movable layer 404b to a neutral position in the absence of forces imposed on the field sensitive element 402, and also constrain the movement of movable layer 404b and field sensitive element 402 to a path along the aforementioned translational axis (i.e., perpendicular to the top surface of support structure 406). The displacement of layer 404b varies an optical passband of the optical cavity by changing the distance between the two partially reflective surfaces (layer 404a and moveable layer 404b). For a time-variant electromagnetic field, the movement produces a corresponding time-variance in the wavelength of that portion of the light spectrum affected by the optical cavity. The resulting optical spectrum or wavelength variation Δλ shown in
In at least some illustrative embodiments, the source of the light entering the optical cavity is preferably a wideband light source as shown in
Similarly, the magnetic field B(z,t) to which the optical transducer is exposed is generated by an electrical potential applied across sensor surfaces 308 and 310 and thus across induction coil 424 positioned above and along the translational axis of field sensitive element 402. In the embodiment shown, the magnetic axis of the induction coil 424 is preferably oriented to be parallel to the magnetic axis of field sensitive element 402, with both magnetic axes also being preferably parallel to the translational axis of field sensitive element 402. Nonetheless, other orientations of induction coil 424 and field sensitive element 402 are contemplated and are within the scope of the present disclosure. Induction coil 424 is electrically coupled to sensor surfaces 308 and 310. As a result, any electrical potential induced by electromagnetic fields within the surrounding formation is applied across induction coil 424. After solving for the gradient of B(z,t), the field B(z,t) can be determined with an appropriate calibration of the transducer.
The piezoelectric cylinder 592 is a hollow cylinder with an inner surface electrode and an outer surface electrode. The piezoelectric material is a substance that exhibits the reverse piezoelectric effect: the internal generation of a mechanical force resulting from an applied electrical field. Suitable piezoelectric materials include lead zirconate titanate (PZT), lead titanate, and lead metaniobate. For example, lead zirconate titanate crystals will change by about 0.1% of their static dimension when an electric field is applied to the material. The piezoelectric cylinder 592 is configured such that a diameter of the outer surface of the piezoelectric cylinder 592 changes when an electrical voltage is applied between the inner and outer surfaces. As a result, the diameter of the outer surface of the piezoelectric cylinder 592 is dependent on the electrical voltage applied across sensor surfaces 308 and 310.
In the embodiment of
This light leakage characteristic can be exploited with a microbend sensor or microbender 660 such as that shown in
In the embodiment of
As previously noted, the light beam modulated by the above-described transducers of EM sensor 300 is encoded and presented to a processing and control system that provides further processing of the collected EM survey data.
Located within processing and control system 750 is a display interface 752, a telemetry transceiver 754, a processor 756, a peripheral interface 758, an information storage device 760, an network interface 762 and a memory 770. Bus 764 couples each of these elements to each other and transports their communications. Telemetry transceiver 754 enables the processing and control system 750 to communicate with the array of sensors 300 via surface interface module 710 and subsea interface module 730 (offshore) and via field interface module 790 (onshore). Network interface 762 enables communications with other systems (e.g., a central data processing facility via the Internet). In accordance with user input received via peripheral interface 758 and program instructions from memory 770 and/or information storage device 760, processor 756 processes telemetry information received via telemetry transceiver 754 to build and combine time-lapse reservoir models in accordance with the disclosed methods and displays the results produced by the models to the user. In at least some illustrative embodiments, the processing and control system 750 is preferably configured by software (e.g., in the form of non-volatile removable media 761) to control the EM monitoring system and to process the EM survey data as described.
In at least some illustrative embodiments, processing and control system 750 of
Continuing to refer to
s
Referring now to both
One or more of the EM sensors 300 modulates the interrogation light beam as it passes through the sensor, and the modulated beam is received from the sensors (block 806; fiber-optic module 782). The EM sensor data is collected and used to produce at least two surveys that are separated in time by at least one month (block 808; inversion module 778), with the survey data being combined to produce a time-lapse earth model of the reservoir (block 810; model 780). The resulting model is then used to simulate the reservoir and predict it future behavior (block 812; model 780) with the simulation results being presented to the user (block 812; block 772), ending the method (block 814).
It should be noted that the use of optical fibers and/or TECs enable other sensor systems to be incorporated into the sensor array, providing additional concurrently collected data to be combined with the survey data produced from EM data collected from the EM sensor 300 array. For example the optical fiber can be used as a sensing device within a distributed temperature sensor system (DTSS), enabling the collection of temperature data at the location of each EM sensor 300 that can be used to temperature calibrate the data collected. Seismic sensors may also be deployed and coupled to the fiber or TEC to provide con currently collected seismic data that may also be combined with the EM sensor survey data. The combination of EM survey, temperature and seismic data enhances the resolution and accuracy of the time-lapse earth model produced. Other types of sensors and data that can be combined with the EM survey data, such as geophysical and production well data, will become apparent to those of ordinary skill in the art, and all such sensors, data and combinations are within the scope of the present disclosure.
Numerous other modifications, equivalents, and alternatives, will become apparent to those skilled in the art once the above disclosure is fully appreciated. For example although the embodiments described included a single subsea interface module coupled to a single sensor array over a single reservoir, other illustrative embodiments may include multiple subsea interface modules, each coupled to multiple arrays deployed over separate regions of a reservoir or over multiple reservoirs within a production field. It is intended that the following claims be interpreted to embrace all such modifications, equivalents, and alternatives where applicable.
Filing Document | Filing Date | Country | Kind |
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PCT/US2014/067777 | 11/26/2014 | WO | 00 |
Publishing Document | Publishing Date | Country | Kind |
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WO2016/085511 | 6/2/2016 | WO | A |
Number | Name | Date | Kind |
---|---|---|---|
4090141 | Leblanc | May 1978 | A |
4222261 | Leblanc et al. | Sep 1980 | A |
4348587 | Tangonan et al. | Sep 1982 | A |
4360272 | Schmadel et al. | Nov 1982 | A |
4376248 | Giallorenzi et al. | Mar 1983 | A |
4433291 | Yariv | Feb 1984 | A |
4596442 | Anderson et al. | Jun 1986 | A |
4622460 | Failes et al. | Nov 1986 | A |
4868495 | Einzig et al. | Sep 1989 | A |
4918371 | Bobb | Apr 1990 | A |
4950883 | Glenn | Aug 1990 | A |
4973845 | Mastrippolito et al. | Nov 1990 | A |
4996419 | Morey | Feb 1991 | A |
5047741 | Laskaris et al. | Sep 1991 | A |
5275038 | Sizer et al. | Jan 1994 | A |
5294788 | Charon et al. | Mar 1994 | A |
5305075 | Bucholtz et al. | Apr 1994 | A |
5317576 | Leonberger et al. | May 1994 | A |
5471139 | Zadoff | Nov 1995 | A |
5511083 | D'Amato et al. | Apr 1996 | A |
5513913 | Ball et al. | May 1996 | A |
5563513 | Tasci et al. | Oct 1996 | A |
5642051 | Babour et al. | Jun 1997 | A |
5691999 | Ball et al. | Nov 1997 | A |
5754284 | Leblanc et al. | May 1998 | A |
5770945 | Constable | Jun 1998 | A |
5804736 | Klauder et al. | Sep 1998 | A |
5844927 | Kringlebotn | Dec 1998 | A |
6188712 | Jiang et al. | Feb 2001 | B1 |
6229308 | Freedman | May 2001 | B1 |
6229808 | Teich et al. | May 2001 | B1 |
6271766 | Didden et al. | Aug 2001 | B1 |
6289740 | Posey, Jr. et al. | Sep 2001 | B1 |
6294917 | Nichols | Sep 2001 | B1 |
6314056 | Bunn et al. | Nov 2001 | B1 |
6325153 | Harrell | Dec 2001 | B1 |
6332109 | Sheard et al. | Dec 2001 | B1 |
6393363 | Wilt et al. | May 2002 | B1 |
6412555 | Sten-Halvorsen et al. | Jul 2002 | B1 |
6463186 | Li et al. | Oct 2002 | B1 |
6534986 | Nichols | Mar 2003 | B2 |
6597481 | Fatehi et al. | Jul 2003 | B1 |
6630658 | Bohnert et al. | Oct 2003 | B1 |
6724469 | Leblanc | Apr 2004 | B2 |
6728165 | Roscigno et al. | Apr 2004 | B1 |
6731114 | Lagabrielle et al. | May 2004 | B1 |
6747743 | Skinner et al. | Jun 2004 | B2 |
6809516 | Li et al. | Oct 2004 | B1 |
6914433 | Wright et al. | Jul 2005 | B2 |
6957576 | Skinner et al. | Oct 2005 | B2 |
6961601 | Matthews et al. | Nov 2005 | B2 |
7077200 | Adnan et al. | Jul 2006 | B1 |
7109717 | Constable et al. | Sep 2006 | B2 |
7151377 | Chouzenoux et al. | Dec 2006 | B2 |
7183777 | Bristow et al. | Feb 2007 | B2 |
7391942 | Loock et al. | Jun 2008 | B2 |
7477160 | Lemenager et al. | Jan 2009 | B2 |
7489134 | Reiderman | Feb 2009 | B2 |
7492168 | Ogilvy et al. | Feb 2009 | B2 |
7673682 | Daily | Mar 2010 | B2 |
7747388 | Mombourquette et al. | Jun 2010 | B2 |
8035393 | Tenghamn et al. | Oct 2011 | B2 |
8058869 | Cranch et al. | Nov 2011 | B2 |
8165178 | Henderson et al. | Apr 2012 | B2 |
8379438 | Tio Castro et al. | Feb 2013 | B2 |
8380439 | Lagmanson et al. | Feb 2013 | B2 |
8437961 | Srnka et al. | May 2013 | B2 |
8633700 | England et al. | Jan 2014 | B1 |
8710845 | Lindqvist et al. | Apr 2014 | B2 |
9008970 | Donderici et al. | Apr 2015 | B2 |
9081114 | Nie et al. | Jul 2015 | B2 |
9091785 | Donderici et al. | Jul 2015 | B2 |
9127531 | Maida et al. | Sep 2015 | B2 |
9181754 | Donderici et al. | Nov 2015 | B2 |
9273548 | Leblanc et al. | Mar 2016 | B2 |
9297767 | Maida, Jr. et al. | Mar 2016 | B2 |
20010023614 | Tubel et al. | Sep 2001 | A1 |
20020063866 | Kersey et al. | May 2002 | A1 |
20030038634 | Strack | Feb 2003 | A1 |
20030057950 | Gao et al. | Mar 2003 | A1 |
20030094281 | Tubel | May 2003 | A1 |
20030205083 | Tubel et al. | Nov 2003 | A1 |
20030205375 | Wright et al. | Nov 2003 | A1 |
20040006429 | Brown | Jan 2004 | A1 |
20040033017 | Kringlebotn et al. | Feb 2004 | A1 |
20040093950 | Bohnert | May 2004 | A1 |
20040104051 | Moriarty et al. | Jun 2004 | A1 |
20040117119 | West et al. | Jun 2004 | A1 |
20040140091 | Gupta | Jul 2004 | A1 |
20050156602 | Conti | Jul 2005 | A1 |
20050206385 | Strack et al. | Sep 2005 | A1 |
20060081412 | Wright et al. | Apr 2006 | A1 |
20060214098 | Ramos | Sep 2006 | A1 |
20060220651 | Clark | Oct 2006 | A1 |
20060250274 | Mombourquette et al. | Nov 2006 | A1 |
20060272809 | Tubel et al. | Dec 2006 | A1 |
20070000912 | Aisenbrey | Jan 2007 | A1 |
20070062696 | Wilson | Mar 2007 | A1 |
20070126594 | Atkinson et al. | Jun 2007 | A1 |
20070228288 | Smith | Oct 2007 | A1 |
20070278008 | Kuckes et al. | Dec 2007 | A1 |
20080041575 | Clark et al. | Feb 2008 | A1 |
20080042636 | Koste et al. | Feb 2008 | A1 |
20080053702 | Smith, Jr. | Mar 2008 | A1 |
20080106265 | Campbell | May 2008 | A1 |
20080123467 | Ronnekleiv et al. | May 2008 | A1 |
20080210426 | Lembcke et al. | Sep 2008 | A1 |
20080241964 | Kaieda et al. | Oct 2008 | A1 |
20080246485 | Hibbs et al. | Oct 2008 | A1 |
20080290873 | Homan et al. | Nov 2008 | A1 |
20080317400 | Petrov | Dec 2008 | A1 |
20090005994 | Srnka et al. | Jan 2009 | A1 |
20090039888 | MacGregor et al. | Feb 2009 | A1 |
20090044977 | Johnson et al. | Feb 2009 | A1 |
20090071080 | Bourgain et al. | Mar 2009 | A1 |
20090102474 | Cranch et al. | Apr 2009 | A1 |
20090179647 | Wang et al. | Jul 2009 | A1 |
20090188665 | Tubel et al. | Jul 2009 | A1 |
20090199630 | Difoggio et al. | Aug 2009 | A1 |
20090237084 | Itskovich et al. | Sep 2009 | A1 |
20090268197 | Perron et al. | Oct 2009 | A1 |
20090296755 | Brown et al. | Dec 2009 | A1 |
20090308657 | Clark et al. | Dec 2009 | A1 |
20100013487 | Bloemenkamp | Jan 2010 | A1 |
20100046002 | Perez et al. | Feb 2010 | A1 |
20100097065 | Itskovich et al. | Apr 2010 | A1 |
20100118657 | Trinh et al. | May 2010 | A1 |
20100134113 | Depavia et al. | Jun 2010 | A1 |
20100185393 | Liang et al. | Jul 2010 | A1 |
20100198519 | Wilt et al. | Aug 2010 | A1 |
20100224362 | Carazzone | Sep 2010 | A1 |
20100237084 | Freed | Sep 2010 | A1 |
20100271030 | Reiderman et al. | Oct 2010 | A1 |
20100277177 | Alumbaugh et al. | Nov 2010 | A1 |
20110017512 | Codazzi | Jan 2011 | A1 |
20110074428 | Wang | Mar 2011 | A1 |
20110083838 | Labrecque | Apr 2011 | A1 |
20110084696 | Tenghamn et al. | Apr 2011 | A1 |
20110088462 | Samson et al. | Apr 2011 | A1 |
20110090496 | Samson et al. | Apr 2011 | A1 |
20110100632 | Dinariev et al. | May 2011 | A1 |
20110139447 | Ramos et al. | Jun 2011 | A1 |
20110158043 | Johnstad | Jun 2011 | A1 |
20110163891 | Wilson et al. | Jul 2011 | A1 |
20110170112 | Gibler et al. | Jul 2011 | A1 |
20110188347 | Thiercelin et al. | Aug 2011 | A1 |
20110198078 | Harrigan et al. | Aug 2011 | A1 |
20110277996 | Cullick et al. | Nov 2011 | A1 |
20110298461 | Bittar et al. | Dec 2011 | A1 |
20110308788 | Ravi et al. | Dec 2011 | A1 |
20120001625 | Yamada et al. | Jan 2012 | A1 |
20120013893 | Maida et al. | Jan 2012 | A1 |
20120014211 | Maida, Jr. et al. | Jan 2012 | A1 |
20120020184 | Wilson et al. | Jan 2012 | A1 |
20120061084 | Sweatman et al. | Mar 2012 | A1 |
20120090827 | Sugiura | Apr 2012 | A1 |
20120111633 | Sunil | May 2012 | A1 |
20120126993 | Samson et al. | May 2012 | A1 |
20120130641 | Morrison et al. | May 2012 | A1 |
20120147381 | Leblanc et al. | Jun 2012 | A1 |
20120147924 | Hall | Jun 2012 | A1 |
20120175513 | Duncan et al. | Jul 2012 | A1 |
20120191353 | Wilt et al. | Jul 2012 | A1 |
20120205103 | Ravi et al. | Aug 2012 | A1 |
20120212229 | Sinclair et al. | Aug 2012 | A1 |
20120223717 | Labrecque | Sep 2012 | A1 |
20120234605 | Donderici et al. | Sep 2012 | A1 |
20120257475 | Luscombe et al. | Oct 2012 | A1 |
20120293179 | Colombo et al. | Nov 2012 | A1 |
20130018585 | Zhdanov et al. | Jan 2013 | A1 |
20130018588 | Zhdanov et al. | Jan 2013 | A1 |
20130032404 | Donderici et al. | Feb 2013 | A1 |
20130033961 | Burnstad | Feb 2013 | A1 |
20130056197 | Maida et al. | Mar 2013 | A1 |
20130105224 | Donderici et al. | May 2013 | A1 |
20130118734 | Csutak et al. | May 2013 | A1 |
20130127471 | Südow et al. | May 2013 | A1 |
20130141102 | Donderici et al. | Jun 2013 | A1 |
20130146756 | Schmidt | Jun 2013 | A1 |
20130166215 | Bittar et al. | Jun 2013 | A1 |
20130169278 | Bittar et al. | Jul 2013 | A1 |
20130207661 | Ellingsrud et al. | Aug 2013 | A1 |
20130245947 | Samsom et al. | Sep 2013 | A1 |
20130248250 | Bittar et al. | Sep 2013 | A1 |
20130249705 | Sharp et al. | Sep 2013 | A1 |
20130279841 | Joinson | Oct 2013 | A1 |
20130293235 | Bloemenkamp et al. | Nov 2013 | A1 |
20130297215 | Rondeleux | Nov 2013 | A1 |
20140032116 | Guner et al. | Jan 2014 | A1 |
20140036628 | Hill et al. | Feb 2014 | A1 |
20140097848 | Leblanc et al. | Apr 2014 | A1 |
20140111348 | Skinner et al. | Apr 2014 | A1 |
20140139225 | Mandviwala | May 2014 | A1 |
20140139226 | Jaaskelainen et al. | May 2014 | A1 |
20140191120 | Donderici et al. | Jul 2014 | A1 |
20140191761 | San Martin et al. | Jul 2014 | A1 |
20140222343 | Samson et al. | Aug 2014 | A1 |
20140244175 | Donderici et al. | Aug 2014 | A1 |
20150137817 | Wilson et al. | May 2015 | A1 |
20150160365 | Donderici et al. | Jun 2015 | A1 |
20150330190 | Wu et al. | Nov 2015 | A1 |
20160187525 | Wilson et al. | Jun 2016 | A1 |
20160259085 | Wilson et al. | Sep 2016 | A1 |
20160266269 | Wilson et al. | Sep 2016 | A1 |
20170227665 | Wilson | Aug 2017 | A1 |
Number | Date | Country |
---|---|---|
1 803 001 | Feb 2012 | EP |
2005085909 | Sep 2005 | WO |
2005085909 | Sep 2005 | WO |
2012100217 | Jul 2012 | WO |
2012145583 | Oct 2012 | WO |
2012145583 | Oct 2012 | WO |
2012177349 | Dec 2012 | WO |
2012177349 | Dec 2012 | WO |
2013012967 | Jan 2013 | WO |
2015160347 | Oct 2015 | WO |
2015178878 | Nov 2015 | WO |
2016085511 | Jun 2016 | WO |
Entry |
---|
PCT International Search Report and Written Opinion, dated Aug. 12, 2015, Appl No. PCT/US2014/067777, “Onshore Electromagnetic Reservoir Monitoring,” Filed Nov. 26, 2014, 17 pgs. |
Colombo, Daniele et al., “Quantifying Surface-To-Reservoir Electromagnetics for Waterflood Monitoring in a Saudi Arabian Carbonate Reservoir”, Geophysics, 78(6) E281-E297, 2013. |
Hibbs, A D. et al., “Advances in Electromagnetic Survey Instrumentation and the Use of a Cased Borehole for Imaging a Deep Formations”, 76th EAGE Conference & Exhibition 2014 Amsterdam RAI, The Netherlands, Jun. 16-19, 2014, 2014, 3 pgs. |
Hibbs, A. D. et al., “Capacitive Electric Field Measurements for Geophysics”, EAGE Conference and Exhibition incorporating SPE EUROPEC 2011, Vienna, Austria, Expanded Abstracts, 2011, 2 pgs. |
Hibbs, Andrew et al., “New Electromagnetic Sensors for Magnetotelluric and Induced Polarization Geophysical Surveys”, SEG Annual Meeting, Expanded Abstracts, 2012, 5 pgs. |
Hordt, A et al., “A First Attempt at Monitoring Underground Gas Storage by Means of Time-lapse Multichannel Transient Electromagnetics”, Geophysical Prospecting, 2000, 48, 489-509, European Association of Geoscientists & Engineers, 2000, 21 pgs. |
Marsala, Alberto F. et al., “First Borehole to Surface Electromagnetic Survey in KSA: Reservoir Mapping and Monitoring at a New Scale”, SPE 146348, 2011, 9 pgs. |
Marsala, Alberto F. et al., “Fluid Distribution Inter-Well Mapping in Multiple Reservoirs by Innovative Borehole to Surface Electromagnetic: Survey Design and Field Acquisition”, IPTC 17045, 2013, 4 pgs. |
Marsala, Alberto F. et al., “Six-Component Tensor of the Surface Electromagnetic Field Produced by a Borehole Source Recorded by Innovative Capacitive Sensors”, Presented at SEG 83rd Annual Meeting, 2013, 5 pgs. |
Schamper, Cyril et al., “4D CSEM Feasibility Study: A Land Example”, SEG Houston 2009 International Exposition and Annual Meeting, 2009, 5 pgs. |
Strack, K M. et al., “Integrating Long-Offset Transient Electromagnetics (LOTEM) with Seismics in an Exploration Environment”, Geophysical Prospecting, 1996, 44, 997-7017, European Association of Geo-scientists & Engineers, 1996, 21 pgs. |
Tseng, H.W. et al., “A Borehole-to-Surface Electromagnetic Survey”, Geophysics, 63(5), 1565-1572, 1998. |
Wirianto, Marwan et al., “A Feasibility Study of Land CSEM Reservoir Monitoring in a Complex 3D Model”, Geophysical Journal International, Piers Online, vol. 6, No. 5, 2010, pp. 440-444. |
Wright, David et al., “Hydrocarbon detection and monitoring with a multicomponent transient electromagnetic (MTEM) survey”, The Leading Edge, 21 (9), 2002, pp. 862-864. |
Zhdanov, Michael S. et al., “Feasibility study of electromagnetic monitoring of CO2 sequestration in deep reservoirs”, SEG Houston 2013 Annual Meeting, DOI http://dx.doi.org/10.1190/segam2013-0694.1, 2013, pp. 2417-2421. |
Canadian Application Serial No. 2,961,172; Examiner's Letter: Aug. 31, 2018, 6 pages. |
Mwenifumbo, et al., “Field Evaluation of a New Borehole Resistivity Probe Using Capacitive Electrodes”, Proceedings of the Symposium on the Applications of Geophysics to Engineering and Environmental Problems, 1999, pp. 859-867. |
Orange, et al., “The Feasibility of Reservoir Monitoring Using Time-Lapse Marine CSEM”, Geophysics, 74 (2), pp. F21-F29. |
Panissod, et al., “Recent Developments in Shallow-Depth Electrical and Electrostatic Prospecting Using Mobile Arrays”, Geophysics, vol. 63, No. 5 (Sep.-Oct. 1998), 1998, pp. 1542-1550. |
Park, et al., “CSEM sensitivity study for Sleipner CO2-injection monitoring”, Energy Procedia 37, SciVerse ScienceDirect, pp. 4199-4206. |
Petia, “Second Generation of Lead-Lead Chloride Electrodes for Geophysical Applications”, Pure and Applied Geophysics, 157, 2000, pp. 357-382. |
Raiche, “A Flow-Through Hankel Transform Technique for Rapid, Accurate Green's Function Computation”, Radio Science, 34 (2), 1999 pp. 549-555. |
Salak0, et al., “Potential Applications of Time-lapse Marine CSEM to Reservoir Monitoring”, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC, London, UK, Jun. 10-13, 2013, 5 pgs. |
Schamper, et al., “4D CSEM Feasibility Study: A Land Example”, SEG Houston 2009 International Exposition and Annual Meeting, 2009, 5 pgs. |
Schmidt-Hatternberger, et al., “Electrical resistivity tomography ERT) for monitoring of CO2 migration—from tool development to reservoir surveillance at the Ketzin pilot site”, Energy Procedia 37, SciVerse ScienceDirect, 2013, pp. 4268-4275. |
Shima, et al., “Developments of Non-Contact Data Acquisition Techniques in Electrical and Electromagnetic Explorations”, Journal of Applied Geophysics, 35, 1996, pp. 167-173. |
Shima, et al., “Fast Imaging of Shallow Resistivity Structures Using a Multichannel Capacitive Electrode System”, SEG Annual Meeting, Expanded Abstracts, pp. 377-380. |
Strack, et al., “Integrating Long-Offset Transient Electromagnetics (LOTEM) with Seismics in an Exploration Environment”, Geophysical Prospecting, 1996, 44, 997-1017, European Association of Geoscientists & Engineers, 1996), 21 pgs. |
Tabbagh, et al., “Determination of Electrical Properties of the Ground at Shallow Depth With an Electrostatic Quadrupole: Field Trials on Archaeological Sites”, Geophysical Prospecting, 41, (1993), pp. 579-597. |
Thiel, et al., “On Measuring Electromagnetic Surface Impedance—Discussions with Professor James R. Wait”, IEEE Transactions on Antennas and Propagation, vol. 48, No. 10, (2000), pp. 1517-1520. |
Timofeeev, et al., “A New Ground Resistivity Method for Engineering and Environmental Geophysics”, Proceedings of the Symposium on the Applications of Geophysics to Engineering and Environmental Problems, (1994), pp. 701-715. |
Tondel, et al., “Remote Reservoir Monitoring in Oil Sands: From Feasibility Study to Baseline Datasets”, CSEG-CSPG-CWLS GeoConvention, Expanded Abstracts, (2013), 5 pgs. |
Tondel, et al., “Reservoir monitoring in oil sands: Developing a permanent cross-well system”, Presented at SEG Annual Meeting, 2011, San Antonio, 5 pgs. |
Tseng, et al., “A Borehole-to-Surface Electromagnetic Survey”, Geophysics, 63(5), pp. 1565-1572, (1998). |
Tumanski, “Induction coil sensors—a reivew”, Institute of Physics Publishing, Measurement Science and Technology. 18 (2007) R31-R46, 2007, 17 pgs. |
Vohra, et al., “Fiber-optic ac electric-field sensor based on the electrostrictive effect”, Optic Letters, vol. 17, No. 5, Mar. 1, 1992, Mar. 1, 1992, 3 pgs. |
Wilt, et al., “Crosswell Electromagnetic Tomography: System Design Considerations and Field Results”, Geophysics, vol. 60, No. 3 (May-Jun. 1995), pp. 871-855. |
Wirianto, et al., “A Feasibility Study of Land CSEM Reservoir Monitoring in a Complex 3D Model”, Geophysical Journal International, Piers Online, vol. 6, No. 5, (2010), pp. 440-444. |
Wright, et al., “Hydrocarbon detection and monitoring with a multicomponent transient electromagnetic (MTEM) survey”, The Leading Edge, 21 (9), 2002, 7 pages. |
Yang, et al., “Optical fiber magnetic field sensors with TbDyFe magnetostrictive thin films as sensing materials”, National Engineering Laboratory for Optical Fiber Sensors, 2009, Optical Society of America, (2009), 6 pgs. |
Zach, et al., “Marine CSEM Time-Lapse Repeatability for Hydrocarbon Field Monitoring”, Presented at SEG Annual Meeting in Houston, (2009), 5 pgs. |
Zhdanov, et al., “3D inversion of towed streamer EM data—A model study of the Harding field and comparison to 3D CSEM inversion”, SEG San Antonio 2011 Annual Meeting, (2011), 5 pgs. |
Zhdanov, et al., “Feasibility study of electromagnetic monitoring of CO2 sequestration in deep reservoirs”, SEG Houston 2013 Annual Meeting, DOI http://dx.doi.org/10.0090/segam2013-0694.1, 2013, pp. 2417-2421. |
Ziolkowski, et al., “Multi-Transient Electromagnetic Repeatability Experiment Over the North Sea Harding Field”, Geophysical Prospecting, 58, (2010), pp. 1159-1176. |
Zonge, et al., “The Effect of Electrode Contact Resistance on Electric Field Measurements”, 55th SEG Annual Meeting, Washington D.C., Expanded Abstracts, (1985), 8 pgs. |
Australian Patent App. No. 2014384700, Examination Report, dated Sep. 14, 2016, 3 pgs. |
PCT Application No. PCT/US2013/075117, International Preliminary Report on Patentability, dated Jun. 23, 2016, 9 pgs. |
PCT Application No. PCT/US2013/075117, International Search Report & Written Opinion, dated Sep. 12, 2014, 12 pgs. |
PCT Application No. PCT/US2014/019228, International Preliminary Report on Patentability, dated Sep. 15, 2016, 10 pgs. |
PCT Application No. PCT/US2014/019228, International Search Report & Written Opinion, dated Nov. 5, 2014, 12 pgs. |
PCT Application No. PCT/US2014/034416, International Preliminary Report on Patentability, dated Oct. 18, 2016, 10 pgs. |
PCT Application No. PCT/US2014/034416, International Search Report & Written Opinion, dated Jan. 19, 2015, 13 pgs. |
PCT Application No. PCT/US2014/038552, International Preliminary Report on Patentability, dated Nov. 22, 2016, 11 pgs. |
PCT Application No. PCT/US2014/038552, International Search Report & Written Opinion, dated Feb. 17, 2015, 13 pgs. |
PCT Application No. PCT/US2014/067774, International Search Report & Written Opinion, dated Aug. 11, 2015, 15 pgs. |
PCT Application No. PCT/US2014/067777, International Search Report & Written Opinion, dated Aug. 12, 2015, 15 pgs. |
PCT Application No. PCT/US2015/063755, International Search Report & Written Opinion, dated Aug. 16, 2016, 12 pgs. |
U.S. Final Office Action, dated Dec. 13, 2016, U.S. Appl. No. 14/760,718, “Time-Lapse Electromagnetic Monitoring,” filed Jul. 14, 2015, 27 pgs. |
Andreis, et al., “Using CSEM to Monitor Production From a Complex 3D Gas Reservoir—A Synthetic Case Study”, The Leading Edge, 30 (11), Sep. 2011, pp. 1070-1079. |
Bergmann, et al., “Surface-downhole electrical resistivity tomography applied to monitoring of CO2 storage at Ketzin, Germany”, Geophysics, vol. 77, No. 6, Nov.-Dec. 2012, pp. B253-B267. |
Berre, et al., “Identification of three-dimensional electric conductivity changes from time-lapse electromagnetic observations”, Journal of Computational Physics, 23, (2011), pp. 3915-3928. |
Bhuyian, et al., “3D CSEM modeling and time-lapse sensitivity analysis for subsurface CO2 storage”, Geophysics 77 (5), (2012), pp. E343-E355. |
Black, et al., “3D inversion of time-lapse CSEM data based on dynamic reservoir simulations of the Harding field, North Sea”, 2011 SEG San Antonio 2011 Annual Meeting, pp. 666-667. |
Black, et al., “3D inversion of time-lapse CSEM data for reservoir surveillance”, SEG Denver 2010 Annual Meeting 716, (2010), 5 pgs. |
Black, et al., “Monitoring of hydrocarbon reservoirs using marine CSEM method”, SEG Houston 2009 International Exposition and Annual Meeting, (2009) 5 pgs. |
Bristow, et al., “A New Temperature Capacitive-Resistivity, and Magnetic-Susceptibility Borehole Probe for Mineral Exploration, Groundwater, and Environmental Applications”, GeologicalSurvey of Canada, Technical Note No. 3, doi: 10.4095/289197, 2011, 12 pgs. |
Carrigan, et al., “Electrical resistance tomographic monitoring of CO2 movement in deep geologic reservoirs”, International Journal of Greenhouse Gas Control, doi: 10.1016/j.ijggc.2013.04.016, (2013), pp. 401-408. |
Christensen, et al., “Monitoring CO2 injection with cross-hole electrical resistivity tomography”, Exploration Geophysics, Butsuri-Tansa (vol. 59, No. 1), Mulli-Tamsa (vol. 9, No. 1), 2005, pp. 44-49. |
Chuprin, et al., “Quantifying factors affecting repeatability in CSEM surveying for reservoir appraisal and monitoring”, SEG Las Vegas 2008 Annual Meeting, 2008, pp. 648-652. |
Colombo, “Quantifying Surface-to-Reservoir Electromagnetics for Waterflood Monitoring in a Saudi Arabian Carbonate Reservoir”, Geophysics, 78(6) E281-E297, (2013). |
Deceuster, et al., “Automated Identification of Changes in Electrode Contact Properties for Long Term Permanent ERT Monitoring Experiments”, Geophysics, vol. 78, No. 2 (Mar.-Apr. 2013), 2011, pp. E79-E94. |
Douma, et al., “A Capacitive-Coupled Ground Resistivity System for Engineering and Environmental Applications: Results of Two Canadian Field Tests”, SEG Annual Meeting, Expanded Abstracts, 1994, pp. 559-561. |
Grard, et al., “A Mobile Four-Electrode Array and Its Application to the Electrical Survey of Planetary Grounds at Shallow Depths”, Journal of Geophysical Research, vol. 96, No. B3, Mar. 10, 1991, pp. 4117-4123. |
Habashy, et al., “Beyond the Born and Rytov Approximations: A Nonlinear Approach to Electromagnetic Scattering”, Journal of Geophysical Research, vol. 98, No. B2, pp. 1759-1775, Feb. 10, 1993. |
Haber, et al., “Enhanced Reservoir Monitoring using Coupled Electromagnetics and Flow Modeling”, Computational Geosciences Inc., Presented at ASEG 23rd International Conference and Exhibition, Melbourne, Australia, 1 pg. |
Hibbs, et al., “Advances in Electromagnetic Survey Instrumentation and the Use of a Cased Borehole for Imaging a Deep Formations”, 76th EAGE Conference &Exhibition 2014 Amsterdam RAI, The NetherlandsJun. 16-19, 2014, (2014), 3 pgs. |
Hibbs, et al., “Capacitive Electric Field Measurements for Geophysics”, EAGE Conference and Exhibition incorporating SPE EUROPEC 2011, Vienna, Austria, Expanded Abstracts, 2011, 2 pgs. |
Hibbs, et al., “New Electromagnetic Sensors for Magnetotelluric and Induce Polarization Geophysical Surveys”, SEG Annual Meeting, Expanded Abstracts, 2012, 5 pgs. |
Holten, et al., “Time lapse CSEM measurements for reservoir monitoring using a vertical receiver-transmitter setup”, 2011 SEG San Antonio 2011 Annual Meeting, (2011), pp. 697-701. |
Hordt, et al., “A First Attempt at Monitoring Underground Gas Storage by Means of Time-lapse Multichannel Transient Electromagnetics”, Geophysical Prospecting, 2000, 48, 489-509, European Association of Geoscientists & Engineers, (2000), 21 pgs. |
Hoversten, et al., “Crosswell Electromagnetic and Seismic Imaging: An Examination of Coincident Surveys at a Steam Flood Project”, Geophysics, 69 (2), (2004), pp. 406-414. |
Kang, et al., “A Feasibility Study of CO2 Sequestration Monitoring Using the MCSEM Method at a Deep Brine Aquifer in a Shallow Sea”, Geophysics 77 (20, 2012, pp. E117-E126. |
Kiessling, et al., “Geoelectrical Methods for Monitoring Geological CO2 Storage; First Results From Cross-Hole and Surface-Downhole Measurements From the CO2Sink Test Site at Ketzin (Germany)”, International Journal of Greenhouse Gas Control, 4, 2010, pp. 816-826. |
Kuras, et al., “Capacitive Resistivity Imaging With Towed Arrays”, Journal of Engineering and Environmental Geophysics, vol. 12, Issue 3, 2007, pp. 267-279. |
Kuras, et al., “Fundamentals of the Capacitive Resistivity Technique”, Geophysics, vol. 71, No. 3 (May-Jun. 2006), 2006, pp. G135-G152. |
Labrecque, et al., “Assessment of Measurement Errors for Galvanic-Resistivity Electrodes of Different Composition”, Geophysics, vol. 73, No. 2 (Mar.-Apr. 2008), 2008, pp. F55-F64. |
Liang, et al., “Joint Inversion of Controlled-Source Electromagnetic and Production Data for Reservoir Monitoring”, Geophysics 77 (5), pp. ID9-ID22. |
Lien, et al., “Sensitivity Study of Marine CSEM Data for Reservoir Production Monitoring”, Geophysics, 73 (4), 2008, pp. F151-F163. |
Maas, et al., “A Fibre Optic Multi-Component Seismic Acquisition System for Permanent Reservoir Monitoring”, Presented at 2008 SPE Asia Pacific Oil and Gas Conference, SPE 115185, (2008), 7 pgs. |
Macnae, “Electric Field Measurements in Air”, SEG Annual Meeting, Expanded Abstracts, 2010, pp. 1773-1777. |
Marsala, et al., “First Borehole to Surface Electromagnetic Survey in KSA: Reservoir Mapping and Monitoring at a New Scale”, SPE 146348, (2011), 9 pgs. |
Marsala, et al., “Fluid Distribution Inter-Well Mapping in Multiple Reservoirs by Innovative Borehole to Surface Electromagnetic: Survey Design and Field Acquisition”, IPTC 17045, (2013), 4 pgs. |
Marsala, et al., “Six-Component Tensor of the Surface Electromagnetic Field Produced by a Borehole Source Recorded by Innovative Capacitive Sensors”, Presented at SEG 83rd Annual Meeting, (2013), 5 pgs. |
Mwenifumbo, et al., “Capacitive Conductivity Logging and Electrical Stratigraphy in a High-Resistivity Aquifer, Boise Hydrogeophysical Research Site”, Geophysics, vol. 74, No. 3 (May-Jun. 2009), 2009, pp. E125-E133. |
Number | Date | Country | |
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20170322333 A1 | Nov 2017 | US |