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A Model-Based Method to Supply Missing Log Information*
By
Michael A. Frenkel1
Search and Discovery Article #40106 (2003)
*Adapted from “extended abstract” for presentation at the AAPG Annual Meeting, Salt Lake City, Utah, May 11-14, 2003.
1 Baker Hughes, Houston, Texas
Joint interpretation of well
logging data requires that all 
logs
involved in the interpretation process be
mutually consistent. We have developed a model-based method that achieves raw
data quality and consistency improvement by means of log data depth matching,
recalibration of abnormal logs, and reconstruction of missing logs.
To perform model-based log
depth matching, we select a log to serve as a depth reference. Using this log,
we generate a reference earth model and calculate all the synthetic logs that
must be depth-matched. Depth matching is accomplished by shifting each log to
the appropriate depth level of the synthetic log in order to match the main
features of both curves. In many practical cases, a single depth shift is not
enough. A more general approach is based on the application of our method to a
sequence of depth windows.
To perform the model-based log
calibrations, we apply the following two-step procedure. At the first step, we
execute the raw data inversion by using the undisturbed (normal) measurements.
At the second step, to reconstruct abnormal or missing logs, we calculate all
the synthetic logs by using the inversion results. These reconstructed synthetic
logs can then be used in the petrophysical interpretation process.
Practical
applications of the method to the raw data are presented. In the first example,
we perform depth matching and recalibration for a suite of old electrical logs
(data from Western Siberia). In the second example, we perform a correction of
abnormal absolute voltage measurements made with the array lateral log tool
(data from Western Australia).
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The evaluation process for oil and gas reservoirs includes the accurate
estimation of underground formation resistivities. The use of array
logging data such as old lateral
In practice, however, especially when we are dealing with the suite of
old electrical
Special correcting procedures must be applied to prepare the raw data
for joint, inversion-based interpretation. Once the
A Model-based Log Data Correction and Restoration Method
In this section, we describe a model-based method to supply missing and
to correct abnormal
Generally, each preprocessing procedure involves three steps. First, we
generate a reference earth model using the accurate log readings or
executing the raw data inversion on a set of measurements assumed to be
valid. Then, we calculate the It should be noted that, for the preprocessing procedures we describe below to be feasible, we must make certain assumptions regarding data quality and availability.
Let us assume that the mud resistivity log (Rm) and the
caliper log are known; we select one log (preferably one more explicit
to the depth features; e.g., focused-type resistivity log LL3) as the
depth reference. To perform depth matching in a simple and fast manner,
we first calculate all the
Depth matching is accomplished by shifting each log to the appropriate
level of the
Let us assume that the Rm and the caliper
The log scaling (calibration) factors determined by the above inversion
procedure can then be used to recalibrate the
This procedure is similar to the previous one. The difference is that we
apply inversion to
Missing log reconstruction procedure
This procedure is quite similar to the offset correction approach. As an
example, we show here how to restore the mud resistivity log (Rm).
Let us assume that the caliper log is known. We select resistivity
In this section, we present two case studies for vertical exploration wells from Western Siberia and the North West shelf region of offshore Western Australia. All depths are relative and given in meters. These case studies will demonstrate practical applications of the logging data correction and reconstruction procedures we have developed.
Case Study 1 - B Russian BKZ Data from Western Siberia
A suite of BKZ
Analysis of the raw data indicated that the The 2-D inversion was next used to determine a picture of the formation resistivity around the borehole over a selected 100-meter interval. The SP log was used for both detecting layer boundary positions and estimating intervals of high shale content. These layers were assumed to be impermeable while constructing an initial resistivity model. Initial resistivity values were determined from the 1-D inversion results. The 2-D resistivity interpretation results are shown in Figure 1. We can see a good match between the raw and theoretical responses (tracks 3 and 4) calculated using the final inverted model. This good data match indicates reliability of the preprocessing results and could also be used for QC of the inversion results.
Case Study 2 B - Array Lateral Log Data from Australia This section covers the array lateral log (HDLL) data interpretation for the E-1 exploration well from offshore Western Australia. The well contained two hydrocarbon columns. In this paper, we present results of resistivity interpretation only for the top column, the Lower Barrow group sand, located at 478.5 - 490.0 m with a gas/oil contact at 484.5 m (Frenkel and Walker, 2001). At a well site, the resistivity image of the formation around the wellbore, derived from the so-called software-focused resistivity (SFR) curves, provides information necessary to delineate permeable zones and supports immediate operational decisions. At a geoscience center, a more detailed image of the formation resistivity around the wellbore can subsequently be derived with a more rigorous 2-D (in case of a vertical well) or 3-D (in case of a deviated well) inversion (Hakvoort et al., 1998). The SFR resistivity curves indicated an anomalous response over the water-bearing section of the Lower Barrow Formation below 480 m. The shallow SFR curves overlay each other, between 0.6 and 0.7 W∙m, while the 20”, 30”, and 40” SFR curves overlay each other at about 1.0 W∙m, with the 50” SFR reading around 1.2 W∙m (Figure 2, track 7). There does not seem to be any explanation for this response, as invasion should have produced a more gradual increase in resistivity. It was suspected, however, that a voltage discrepancy caused by offset measurements was the reason for these unexpected SFR results. The 2-D inversion performed with the first differences only is immune to offsets in the absolute voltages. The inversion results, Lxo, Rxo, and Rt curves (depth of invasion, resistivity of invaded zone, and resistivity of uncontaminated zone, respectively), are displayed in Figure 2 (tracks 3-4). Evident in the hydrocarbon zone is the minor amount of separation between the Rxo and Rt curves. This indicates a low degree of invasion, probably due to a strong mud cake. It was then possible to simulate all the theoretical curves, including the first differences and voltages, using the final earth model derived by inversion. The excellent match of the array lateral log first differences (track 5) indicates the reliability of the inversion results. It is evident that the match of the absolute voltage V4 (track 6) is far from satisfactory.
To investigate this problem, let us consider a 3-layer Track 1 shows V4 accurately calculated for this model and V4S shifted by a constant 25% of its value calculated far away from the central layer. This means the actual V4 offset at the central part of the model is much less than at the shoulders, and is about 4%. Track 2 shows the SFR curves calculated using the offset voltage V4S. They exhibit the same abnormal behavior as the field SFR curves shown on Figure 2 (track 7).
In the model case, the accurate SFR curves calculated using the accurate
voltage V4, provide a correct radial resistivity profile (Figure 3,
track 3). In the field case, the SFR curves recalculated using the
A model-based method that allows for fast logging data consistency
improvement by means of log data depth matching, recalibrating abnormal
Frenkel, M.A., et al., 1996, Rapid well-site inversion of full-spectrum array induction data: Paper SPE 36505, presented at the SPE ATCE, Denver.
Frenkel, M.A., Mezzatesta, A.G., and Strack, K.-M., 1997,
Enhanced interpretation of Russian and old electrical resistivity
Frenkel, M.A., and Walker, M.J., 2001, Impact of array
lateral Hakvoort, R.G., et al., 1998, Field measurements and inversion results of the high-definition lateral log: Paper C in 39th SPWLA ALS. Harrison, B., ed., 1995, Russian-style formation evaluation: LPS, London. Hilchie, D.W., 1979, Old electrical log interpretation: Golden, Colorado. SPWLA, Houston Chapter, 1979, The art of ancient log analysis. Wiltgen, N.A., and Truman, R.B., 1993, Russian lateral (BKZ) analysis: Paper SPE 26433, presented at the SPE ATCE, Houston.
Thanks to Baker Atlas for granting permission to publish this work, to Woodside Energy for permission to use the HDLL field data, and to Petrophysical Solutions for providing the BKZ field data. The author is indebted to Rashid Khokhar, Alberto Mezzatesta, and Mike Walker for their contribution throughout this development, Sven Treitel for review of the paper and numerous precious comments, and Karen Bush for her help in the paper production. |
Figure 1. Interpretation results of the BKZ resistivity 
Figure 2. Raw 
Figure 3. Effect of the reference voltage V4 offset on the SFR curves.
Model parameters: Rm=0.06 