Patent Application
20160370480
Not Applicable.
This disclosure is related to the field of well logging instruments having sensors that make measurements usable to generate an equivalent of a visual image of a wall of a wellbore through which the instrument is moved. More specifically, the disclosure relates to methods and systems for processing such measurements to automatically identify certain types of geologic features from the measurements. This section is intended to introduce the reader to various aspects of the technical field of the disclosure that may be related to the subject matter described and/or claimed below. This section is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this context, and are not to be construed as admissions of prior art.
Well logging instruments are used in wellbores drilled through subsurface formations to make, for example, measurements of selected physical parameters of the formations to infer properties of the formations surrounding the wellbore and the fluids in void spaces in the formations. Well logging instruments known in the art include electromagnetic tools, nuclear tools, acoustic tools, and nuclear magnetic resonance (NMR) tools, though various other types of tools for evaluating formation properties are also known.
Well logging instruments may be deployed in and moved along the interior of a wellbore on an armored electrical cable (“wireline”) after the wellbore has been drilled. Present versions of such “wireline” well logging instruments are still used extensively. However, as the demand for information during the drilling of a wellbore continues to increase, measurement-while-drilling (MWD) tools and logging while drilling (LWD) instruments have been developed to fulfill such demand. MWD tools are generally defined as those making measurements of drilling parameters such as axial force (weight) on a bit used to drill the wellbore, torque applied to a drill string, wellbore temperature, wellbore fluid pressure, and well trajectory direction and inclination. LWD instruments are generally defined as those which make formation parameter measurements such as electrical resistivity, fractional volume of pore space in the formations (“porosity”), acoustic velocity, density, neutron hydrogen index and/or capture cross-section and NMR relaxation time distributions, among other measurements. MWD and LWD instruments often have sensors similar in nature to those found in wireline instruments (e.g., transmitting and receiving antennas, sensors, etc.), but MWD and LWD tools are designed and constructed to operate in the harsh environment of wellbore drilling.
Well logging measurements may be processed to form images. Such processing may include plotting values of one or more well logging measurements in the form of gray scale or color scale with respect to both axial position in the wellbore (measured depth) and circumferential orientation within the wellbore. Logging-while-drilling (LWD) images acquired in highly inclined or horizontal wellbores may be characterized by various features that are sensitive to formation geologic structure near the wellbore. In well log data processing known in the art, image features commonly referred to as “sinusoids”, “bulls-eyes”, or “reverse bulls-eyes” may extracted from the images manually. However, manual feature extraction is time consuming and prone to user bias. This is of particular concern in highly inclined and/or horizontal wells, where small errors in determining formation layering angle with respect to horizontal (“structural dip”) may translate into large errors in calculated formation reservoir volumetrics. See, for example, Q. R. Passey et al., Overview of High-Angle and Horizontal Well Formation Evaluation: Issues, Learnings, and Future Directions, SPWLA 46th Annual Logging Symposium, Jun. 26-29, 2005. Furthermore, “bulls-eye” features have been observed extending for hundreds of feet in measured depth (axial length along the wellbore). It is therefore important to account for changes in both wellbore trajectory inclination and geodetic or geomagnetic direction (“azimuth”), and formation dip/azimuth, in the structural interpretation of such formations.
A summary of example embodiments disclosed herein is set forth below. It should be understood that these embodiments are presented only to provide the reader with a brief summary of the subject matter and that the disclosed embodiments are not intended to limit the scope of this disclosure. The disclosure may encompass a variety of aspects and embodiments that may not be set forth herein.
The present disclosure sets forth example methods for automatic structural interpretation of bulls-eye and sinusoidal features observed in logging while drilling (LWD) images acquired in highly inclined and/or horizontal wellbores. In accordance with example embodiments, the method is based on an automatic workflow for extracting smooth contours from LWD images that demarcate boundaries of structural features, followed by projection of the boundary contours to three-dimensional (3D) point clouds in the wellbore coordinate system for structural interpretation. The method may characterize both sinusoidal features and bulls-eye features, taking into account variations of formation dip/azimuth, or wellbore inclination/azimuth, on the topology of a structural feature. Compared to methods known in the art prior to the present disclosure, methods described in the present disclosure may have a processing time of as little as a few seconds for a hundred feet (30 meters) of wellbore image data. Accordingly, example methods disclosed herein may be sufficiently fast for use in real-time analysis and interpretation, or to provide constraints for physics-based well log data inversion processing.
In accordance with aspects of the present disclosure, the effect of well logging instrument eccentering on the accuracy of formation dip estimated from sinusoidal features may also be quantified. Based on a geometric model, it has been found that logging instrument eccentering perturbs the shape of an image of a geologic feature from a simple sinusoid. However, when eccentering is ignored, it has been observed that errors in estimated apparent relative dip and apparent azimuth are less than a few tenths of a degree for many highly inclined or horizontal well logging situations.
In one embodiment, a method includes acquiring an azimuthally substantially continuous wellbore image using a well logging instrument disposed in a wellbore penetrating a subsurface formation. The method includes using a processor to process the azimuthally substantially continuous borehole image for extraction of contours, to group the extracted contours into clusters corresponding to a single transition zone, and to map the extracted contours having a measured depth interval (axial extent) that is greater than a length-scale over which the dip of the subsurface formation varies to a three-dimensional space corresponding to a coordinate system associated with the wellbore. Extracted contours having a measured depth interval that is less than the length-scale over which the dip of the subterranean formation varies, using the processor to estimate relative formation dip and apparent azimuth based on a first harmonic approximation of a contour.
The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.
One or more example embodiments according to the present disclosure are described below. The disclosed embodiments are merely examples of the presently disclosed subject matter. Additionally, in an effort to provide a concise description of such embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such implementation, as in any engineering or design project, numerous implementation-specific decisions may be made to obtain the developers' specific objectives, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such development efforts might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of the present disclosure.
When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The embodiments discussed below are intended to be examples that are illustrative in nature and should not be construed to mean that the specific embodiments described herein are necessarily preferential in nature. Additionally, it should be understood that references to “one embodiment” or “an embodiment” within the present disclosure are not to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.
A drill string 12 is suspended within the borehole 11 and has a bottom hole assembly (BHA) 100 which includes a drill bit 105 at its lower end. A surface system includes a platform and derrick assembly 10 positioned over the wellbore 11, with the platform and derrick assembly 10 including a rotary table 16, kelly 17, hook 18 and rotary swivel 19. In a drilling operation, a drill string 12 is rotated by the rotary table 16 (energized by means not shown), which engages the kelly 17 at the upper end of the drill string 12. The drill string 12 is suspended from a hook 18, attached to a traveling block (also not shown), through the kelly 17 and a rotary swivel 19 which permits rotation of the drill string 12 relative to the hook 18. As is well known, a top drive system could be used in other embodiments of a drilling system instead of the kelly, rotary swivel and rotary table.
Drilling fluid (“mud”) 26 may be stored in a pit 27 formed at the well site or a tank. A pump 29 moves the drilling fluid 26 to the interior of the drill string 12 via a port in the swivel 19, which causes the drilling fluid 26 to flow downwardly through the drill string 12, as indicated by the directional arrow 8 in
The drill string 12 includes a bottom hole assembly (BHA) 100 which in an example embodiment may comprise one MWD module 130 and multiple LWD modules 120 (with reference number 120A depicting a second LWD module). As used herein, the term “module” as applied to MWD and LWD devices may be understood to mean either a single instrument or a suite of multiple instruments contained in a single modular device. Additionally, the BHA 100 includes the drill bit 105 and a steering mechanism 150, such as rotary steerable system (RSS), a motor, or both.
The LWD modules 120 may be disposed in a drill collar or in respective drill collars and may include one or more types of well logging instruments. The LWD modules 120 may include devices for measuring, processing, and storing information, as well as for communicating with surface equipment. By way of example, the LWD module 120 may include, without limitation, a nuclear magnetic resonance (NMR) logging tool, an electromagnetic induction and/or electromagnetic propagation resistivity tool, a nuclear tool (e.g., gamma-ray), a laterolog resistivity tool, a photoelectric factor tool, a neutron hydrogen index tool, a neutron capture cross-section tool and/or a formation density tool. The LWD module 120, in general, may include any type of logging tool suitable for acquiring measurements that may be processed to generate wellbore images.
The MWD module 130 may also be housed in a drill collar, and can contain one or more devices for measuring characteristics of the drill string and drill bit. In the present embodiment, the MWD module 130 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick/slip measuring device, a direction measuring device, and an inclination measuring device (the latter two sometimes being referred to collectively as a direction and inclination package).
The MWD tool 130 may also include a telemetry apparatus (not shown).
The MWD tool 130 may also include an apparatus (not shown) for generating electrical power for the MWD tool and the LWD tool(s). In some embodiments, such apparatus may include a turbine generator powered by the flow of the drilling fluid 26. It is understood, however, that other power and/or battery systems may be used.
The operation of the platform and derrick assembly 10 of
While the example wellsite system shown in
As described above, embodiments according to the present disclosure relate to systems and methods for automatic interpretation of structural features observed in wellbore images made from well logging measurements acquired in a wellbore penetrating subterranean formations. In particular, the methods disclosed herein for automatic structural interpretation are applicable to, but are not limited to high-angle and horizontal wells. Other methods are disclosed in International (PCT) Application Publication No. WO2013/066682, filed on Oct. 24, 2012 and entitled “Inversion-Based Workflow for Processing Nuclear Density Measurements In High-Angle and Horizontal Wells.”
By way of background information,
From three dimensional (3D) geometry, it can be shown that when a centralized (disposed coaxially in a wellbore) well logging instrument crosses a planar formation layer boundary having substantially constant inclination and substantially constant azimuth, an image generated from the well logging measurements may be characterized by a feature whose shape is described by a simple sinusoid:
l(Θ)=l0+(rbh+EPL)(tan βr cos Θ+tan βr tan αa sin Θ), (1)
tan αa=sin(βt−βr)tan(αt−α)/sin βr, (2)
tan(βt−βr)=tan β cos(αt−α), (3)
where Θ is the tool sensor azimuth, βr is apparent relative dip (the angle between the tool axis and a line normal to the formation layer boundary, measured at the well azimuth), αa is apparent relative azimuth, αt is well azimuth, βt is well inclination, β is true layer dip, a is true layer azimuth, rbh is the borehole radius, and EPL is the so-called “effective penetration length” of the well logging tool. It is noted that the EPL reflects the fact that the tool measures properties of the formation within a finite volume-of-investigation that extends laterally into the formation beyond the wellbore wall. The variables and their symbols are summarized below in Table 1.
The shape of a feature in an image generated from well logging measurements will differ from a simple sinusoid if the relative formation dip varies as the well logging tool crosses the layer boundary, for example, due to variations of formation layer dip or well trajectory. Because the image sinusoid amplitude is proportional to tan βr, departures from simple sinusoidal shapes are more likely to occur when the local relative dip βr approaches 90°, i.e., when the wellbore trajectory is close to parallel to the layer boundary. An example of a non-sinusoidal feature is often referred to as a “bulls-eye” feature. Bulls-eye features may appear during near-parallel drilling when the relative dip changes polarity from down-section (βr<90°) to up-section (βr>90°). A bulls-eye feature is shown in the density image track in
In accordance with embodiments according to the present disclosure, a process for structural interpretation of sinusoidal and bulls-eye features observed in LWD images is set forth below. The process may include at least the actions described below as applied to a “noisy” synthetic density image, shown in
1. Contour Extraction
As may be observed in
Generally, contours that are either open or closed are computed. As the terms are used herein, an “open” contour may be generally regarded as a contour that extends from Θ=0° to Θ=360°, where Θ is the well logging sensor azimuth, and a “closed” contour is one that forms a closed loop in the interior of the image. The image may be rotated by Θ=180° to capture reverse bulls-eye features. As can be appreciated, contours of the image may be computed using any suitable contour extraction algorithm, such as a marching-squares algorithm (see, e.g., Lorenson et al., “Marching Cubes: A High Resolution 3D Surface Construction Algorithm,” SIGGRAPH '87 Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, vol. 21, pp. 163-169, July 1987), square tracing algorithm, Moore-Neighbor algorithm, radial sweep algorithm, Theo Pavlidis' algorithm, asymptotic decider algorithm, cell-by-cell algorithm, or any suitable computer graphics contouring algorithm or a combination of such algorithms. Contour extraction is shown in the left hand most track in
The contour extraction process may fail to detect a feature if there are very large deviations in the values of adjoining pixels of a structural feature. For example, in LWD resistivity images, the upper quadrant of the image may be excluded for the calculation of contours. The exclusion of the upper quadrant improves reliability of the contour extraction because, unlike density images, resistivity images are generally not compensated for mud standoff effects. Therefore, sinusoidal features may not be continuous across the upper quadrant of a resistivity image. Upper quadrant as used herein is intended to mean a circumferential or azimuthal segment of the wellbore wall subtending an azimuthal angle of ¼ of the full circumference (90 degrees) and being centered about the gravitationally uppermost point of the wellbore circumference. Thus the track scales for the three image tracks in
2. Contour Filtering
Noise in the wellbore images may lead to the extraction of a large number of spurious contours that do not correlate with the transition zone of any actual geologic feature in the image. Noise also results in extracted contours having small-scale waves and/or large meanderings away from the transition zone. In accordance with an example embodiment, the present method may use one or both of the following example filtering processes to help reduce spurious contours and contour waves.
First, it is noted that bulls-eye features generally have a minimum extent in measured depth. This can be better understood if Lmin is defined to be the characteristic length-scale over which the well inclination may vary (dogleg severity, that is, angular change in wellbore trajectory with respect to axial span, places a lower limit on the length-scale), or the length-scale over which the formation dip varies, whichever is smaller (usually the former is limiting). A bulls-eye feature having a measured depth extent (axial length along the wellbore) less than Lmin thus generally does not manifest itself in the image because both the well inclination and formation dip would be nearly constant over the length Lmin.
Thus, a structural feature with length-scale smaller than Lmin must be a sinusoid, and the corresponding threshold relative dip βrmin for sinusoidal features to manifest themselves in the image would be given by βmin=tan−1(0.5Lmin/(rbh+EPL)). In accordance with an embodiment of the present method, any closed contour with a maximum extent in measured depth less than Lmin is considered to be not representative of an actual formation feature and may be deleted. As an example βrmin=88° is used herein.
Second, using the assumption that real geologic features have substantially smooth boundaries, the present example method may compute a low-order Fourier series approximation of each contour using, for example, least-squares minimization and delete any contours for which the quality of fit, as measured by the correlation coefficient R2, is lower than a specified threshold R2min.
In one example, an open contour may be approximated using only the first harmonics in the tool azimuth Θ:
y
open(Θ)=y0+A1 cos Θ+B1 sin Θ, (4)
It can be observed from Eq. 1 that the above approximation is exact if the well logging instrument is centralized (coaxial with) in the wellbore and the relative dip is constant as the well logging instrument crosses a layer boundary. However, when the well is drilled near-parallel to a formation layer boundary, variations of well inclination or formation layer dip may perturb the shape of the contour from a simple sinusoid. To capture that behavior, if an open contour has measured depth extent greater than Lmin, second harmonics may be included in the approximation.
For a closed contour, the foregoing may be approximated using the first m harmonics in the elliptical polar angle γ:
For the example results presented in this disclosure, m=4 was used. Using the elliptical polar angle as the Fourier expansion variable instead of Cartesian polar angle allows more efficient approximation of closed contours that have an aspect ratio greater than unity (e.g., the aspect ratio of a closed contour is defined as its maximum measured depth extent to its maximum azimuthal extent). Closed contours observed in LWD images are often highly elongated in the measured depth (along the length of the wellbore) direction. For example, the measured depth extent (˜Lmin) is typically much greater than their maximum azimuthal extent (˜2πrbh). Contour filtering is shown in
3. Contour Clustering
In contour clustering, extracted, filtered contours may be automatically grouped into clusters such that each cluster corresponds to a single transition zone. In accordance with one example embodiment, a “log-squaring” algorithm may be used to identify locations of transition zones in a well log derived by azimuthal averaging of the pixels in the bottom quadrant of the image. Contours that are sufficiently close to a transition zone are grouped into a single cluster and their Fourier coefficients are averaged to derive a single smooth contour demarcating the boundary of a feature. Contour clustering is illustrated in the third track in
4. Contour Projection and Dip Estimation
For open contours that have a measured depth extent less than Lmin, based on the geometric model for sinusoidal features in the previous section, apparent relative dip βrest and apparent azimuth αaest may be estimated from the amplitudes of the first harmonics in Eq. 4:
tan βrest=A1/(rbh+EPL), (6)
tan αaest=B1/A1 (7)
True layer dip and azimuth may be determined from βrest and αaest using the geometric model.
For closed contours and open contours that have measured depth extent greater than Lmin, a two-dimensional image contour may be projected into a three-dimensional cloud point referenced in the well coordinate system. Such projection methods may include one such as described in Liu et al., Improved Borehole Image Dip Calculation In Irregularly Shaped and Curved Boreholes in High-Angle and Horizontal Wells, SWPLA 51st Annual Logging Symposium, Jun. 19-23, 2010. The projections take into account the well inclination, azimuth, and borehole geometry along the contour. In accordance with the presently described techniques, true dip and true azimuth of a feature may be estimated by least-squares fitting a plane to the 3D point cloud. The residual of the fit may be used to identify non-planar features. Further, open contours having measured depth extent less than Lmin can also be evaluated using the above-mentioned projection technique, which may simplify the computations/logic and also avoid the intermediate computations of Eqs. 6 and 7. Estimation of layer dip and azimuth using the foregoing process elements is shown in
5. Effect of Tool Eccentering on Accuracy of Dip Estimated from Sinusoidal Features
As explained above, in developing the methods disclosed herein, the effect of tool eccentering (that is, displacement of the instrument axis from the wellbore axis) on the accuracy of dip estimation was studied. For an eccentered tool, a model for the wellbore shape may be obtained by replacing the constant borehole radius rbh in Eq. 1 with the variable r(Θ):
l(Θ)=l0+(r(Θ)+EPL)(tan βr cos Θ+tan βr tan αa sin Θ), (8)
r(Θ)=rbh(e cos(Θ−φ)+√{square root over (1−e2 sin2(Θ−φ))}), (9)
e≡(rbh−rtool)/rbh−t/(2rtool+t), (10)
where rtool is the well logging tool radius, φ is the touching angle, t is the maximum standoff, and e is the tool eccentering. The eccentering geometry and definitions of the foregoing parameters are shown in
As described above, example methods for estimating dip from a sinusoidal feature includes extracting a contour from the image that traces the shape of the feature, and estimating apparent relative dip βrest and apparent azimuth αaest from the amplitude of the first harmonics of the contour using, for example, Equations 6 and 7. However, assuming that the contour is in reality described by the curve l(Θ) in Equation 8, the parameters βrest and αaest would be accurate when e=0, and would be erroneous if the tool were eccentered. To understand the behavior of the error, one may use the fact that e<1 to expand 40) as a power series of e:
where for convenience of analysis, it is assumed that the touching angle φ is zero.
Substituting the above expression for 40) in Eq. 8, multiplying out the various terms and expressing as a Fourier series in terms of angle θ results in the expression:
l(Θ)=γ0+Â1 cos Θ+̂B1 sin Θ+Â2 cos 2Θ+ . . . (12)
where the amplitude Â1 and ̂B1 of the first harmonics are:
Â
1˜(rbh(1−e2/4)+EPL)tan βr, (13)
̂B 1˜(rbh(1−3e2/4)+EPL)tan αa tan βr.(14)
When comparing Eqs. 13-14 with Eqs. 6-7, it can be observed that errors in the estimated relative dip and apparent azimuth have a relatively weak, second-order dependence on tool eccentering e.
The above analysis may be independently validated by generating synthetic contours using Eq. 8 for different values of [φ,e,βr,αa] and extracting harmonics of l(Θ) numerically.
For the present example, it can be observed that the error in relative dip and apparent azimuth are both less than 0.2°, for e<0.2, 0°<φ<90°, 70°<βr<90°, and −10°<αr<10°. This is a conservative bound. For example, for an 8.25 inch diameter tool, e<0.2 is equivalent to a maximum standoff t<2.1 inches, which is typically much larger than typical standoff observed in practice.
6. Results for Actual Wellbore Data
The disclosed method may enable detecting contours for structural features, which are then projected into three-dimensional space of the wellbore for characterization of formation structure. In one example, a bulls-eye feature in the image may be shown to correspond to a non-planar structure intersected by the wellbore. In practice, it has been found that the processing time is a few seconds for a hundred feet (30 meters) of measured depth of well log data, thus enabling the disclosed method to be fast and efficient when compared to certain other techniques for structural interpretation of wellbore image data. A summary of the input parameters and their values for the results are set forth in Table 2.
As may be understood, the various techniques described above relating to automatic structural interpretation of sinusoidal, bulls-eye, and/or reverse bulls-eye features observed in azimuthal borehole images are provided as examples. Accordingly, it should be understood that the present disclosure should not be construed as being limited to just the examples provided herein. Further, it should be appreciated that automatic structural interpretation techniques according to the present disclosure may be implemented in any suitable manner, including hardware (suitably configured circuitry), software (e.g., via a computer program including executable code stored on one or more tangible computer readable medium), or by using a combination of both hardware and software elements. Further, it should be expressly understood that the various automatic structural interpretation techniques described herein may be implemented on a downhole processor (e.g., a processor that is part of a wellbore deployed logging/imaging tool), with the results communicated to the surface by any suitable telemetry technique. Additionally, in other embodiments, borehole image data may be transmitted from the instrument in the wellbore to surface using telemetry, and the automatic structural interpretation methods may be performed at the surface using a surface-deployed computer (e.g., part of control system 154 in
The specific embodiments described above are only intended to serve as examples. It will be appreciated that many modifications and other embodiments will come to the mind of one skilled in the art having the benefit of the foregoing description and the associated drawings. Accordingly, it is understood that various modifications and embodiments are intended to be included within the scope of the appended claims, and that the scope of the present disclosure shall be limited only by such appended claims.
Priority is claimed from U.S. Provisional Application 61/946,662 filed on Feb. 28, 2014.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2015/017930 | 2/27/2015 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 61946662 | Feb 2014 | US |
