Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
2000-06-12
2003-09-23
McElheny, Jr., Donald E. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
Reexamination Certificate
active
06625541
ABSTRACT:
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC
A computer program listing of the preferred embodiment of the present invention has been separately submitted on a compact disc entitled “Computer Program Listing Appendix.” The computer program listing contained therein is incorporate by reference herein in its entirety.
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention relates to the exploration of hydrocarbon wells. More particularly, the invention relates to methods for processing waveforms detected by a downhole tool, particularly a sonic logging tool.
2. State of the Art
Sonic logging is a well developed art, and details relating to sonic logging tools and techniques are set forth in “Porosity Logs”;
Schlumberger Log Interpretation Principles/Applications
, Chapter 5, Schlumberger Educational Services, Texas (1987); A. Kurkjian, et al., “Slowness Estimation from Sonic Logging Waveforms”,
Geoexploration
, Vol. 277, pp.215-256 (1991); C. F. Morris et al., “A New Sonic Array Tool for Full Waveform Logging,” SPE-13285, Society of Petroleum Engineers (1984); A. R. Harrison et al., “Acquisition and Analysis of Sonic Waveforms From a Borehole Monopole and Dipole Source . . . ” SPE 20557, pp. 267-282. (Set. 1990); C. V. Kimball and T. L. Marzetta, “Semblance Processing of Borehole Acoustic Array Data”,
Geophysics
, Vol. 49, pp. 274-281 (March 1984); U.S. Pat. No. 4,131,875 to Ingram; and U.S. Pat. No. 4,594,691 to Kimball et al., all of which are hereby incorporated by reference herein in their entireties. A sonic logging tool typically includes a sonic source (transmitter), and a plurality of receivers which are spaced apart by several inches or feet. In the borehole arts, a sonic signal is transmitted from a sonic source and received at the receivers of the borehole tool which are spaced apart from the sonic source, and measurements are made every few inches as the tool is drawn up the borehole. The sonic signal from the transmitter or source enters the formation adjacent the borehole, and the arrival times and perhaps other characteristics of the receiver responses are recorded. Typically, compressional (P-wave), shear (S-wave) and Stoneley (fluid) arrivals and waves are detected by the receivers and are processed either downhole or uphole. The information which is recorded is typically used to find formation parameters such as formation slowness (the inverse of sonic speed) and semblance, from which pore pressure, porosity, and other determinations can be made. In certain tools such as the DSI (Dipole Sonic Imager) tool (a trademark of Schlumberger), the sonic signals may even be used to image the formation.
Many different techniques for processing the sonic wave signals are known in the art in order to obtain information regarding the borehole and/or formation. Typically, the processing involves digitizing the received signal at a desired sampling rate and then processing the digitized samples according to desired techniques. Examples may be found in U.S. Pat. No. 4,594,691 to Kimball et al. and the references cited therein, as well as in articles such as A. R. Harrison et al., “Acquisition and Analysis of Sonic Waveforms From a Borehole Monopole and Dipole Source . . . ” SPE 20557, pp. 267-282 (Sept. 1990).
For some time now, compressional slowness has been computed using Slowness-Time Coherence (STC) processing. C. V. Kimball and T. L. Marzetta, “Semblance Processing of Borehole Acoustic Array Data”,
Geophysics
, Vol. 49, pp. 274-281 (March 1984). In STC processing, the measured signal is time window “filtered” and stacked, and a semblance function is computed. The semblance function relates the presence or absence of an arrival with a particular slowness and particular arrival time. If the assumed slowness and arrival time do not coincide with that of the measured arrival, the semblance takes on a smaller value. Consequently, arrivals in the received waveforms manifest themselves as local peaks in a plot of semblance versus slowness and arrival time. These peaks are typically found in a peak-finding routine discussed in the aforementioned article by Kimball and Marzetta.
Prior art
FIG. 1
illustrates an array of eight waveforms recorded by a sonic tool at a given depth. Such an array is referred to as “a frame”. Each frame is processed using STC. The first stage in STC is stacking or beamforming of the waveform, to compute semblance, a two-dimensional function of slowness and time which is illustrated in prior art FIG.
2
and is generally referred to as the STC slowness-time plane. The semblance function is given by Equation (1) where x
i
(t) is the waveform recorded by the i-th receiver of an M-receiver equally spaced array with inter-receiver spacing &Dgr;z. The array of waveforms {x
i
(t)} acquired at depth z constitutes a single frame of data.
ρ
(
τ
,
p
)
=
∫
τ
-
τ
2
τ
+
τ
2
&LeftBracketingBar;
∑
k
=
0
M
-
1
x
i
(
t
-
k
Δ
zp
)
&RightBracketingBar;
2
ⅆ
t
M
∫
τ
-
τ
2
τ
+
τ
2
∑
k
=
0
M
-
1
&LeftBracketingBar;
x
i
(
t
-
k
Δ
zp
)
&RightBracketingBar;
2
ⅆ
t
(
1
)
The semblance &rgr;(&tgr;,p) for a particular depth z is a function of time &tgr; and slowness p. More particularly, it is the quotient of the beamformed energy output by the array at slowness p (the “coherent energy”) divided by the waveform energy in a time window of length T (the “total energy”).
The second step in STC processing is to identify peaks in the slowness-time plane with waveform arrivals propagating across the array. Peaks are identified by sweeping the plane with a peak mask. The peak mask is a parallelogram having a slope which corresponds to the transmitter-receiver spacing. A peak is defined as a maximum within the mask region. For each peak, five variables are recorded: the slowness coordinate p, the time coordinate &tgr;, the semblance &rgr;(&tgr;,p), the coherent energy (the numerator of Equation 1), and the total energy (the denominator of Equation 1).
The third step in STC processing is called “labeling” or “classification” and this involves a classification of the peaks found in the second step. The peaks will be classified as corresponding to compressional (P-wave), shear (S-wave) or Stoneley arrivals. Correct classification of the peaks is a difficult process for a number of reasons. Some of the peaks may correspond to spatial aliases rather than the arrival of real waveforms. Some of the peaks may actually be two peaks close together. In general, the problem with state-of-the-art labeling is that small changes in data can cause large differences in the final classification. For example, one of the measures used to classify peaks is the “slowness projection” which is given by Equation (2) and illustrated in prior art FIG.
3
. The slowness projection I(p,z) is essentially slowness as a function of depth z which is found at maximum semblance over time &tgr;.
I
(
p
,
z
)
=
max
τ
ρ
(
τ
,
p
,
z
)
(
2
)
The slowness projection (
FIG. 3
) is currently used as a qualitative check on the slowness logs (prior art
FIG. 4
) output by the labeling function. Bright tracks in the slowness projection may be identified with the arrival of waveforms. The logs are superimposed on the slowness projections to determine whether the logs are consistent with the slowness projections. In certain regions where the arrival is weak or absent, the intensity of the track diminishes or becomes diffuse. For example, as shown in
FIG. 4
, the compressional and shear arrivals in a particular borehole seem to vanish at about 3400 feet and deeper. According to actual state-of-the-art algorithms, the classification of shear slowness fails at approximately 3410 feet and the classification of compressional arrival fails at approximately 3425 feet. While abrupt changes in the output of the labelling f
Shenoy Ramachandra Ganesh
Valero Henri-Pierre
Batzer William B.
DeStefanis Jody Lynn
Gordon David P.
McElheny Jr. Donald E.
Schlumberger Technology Corporation
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