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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 logs (e.g., suite of Russian BKZ logs)
(SPWLA, Houston Chapter, 1979; Hilchie, 1979; Wiltgen and Truman, 1993;
Harrison, 1995; Frenkel et al., 1997) or of modern array logging
measurements (e.g., array induction and array lateral log) (Frenkel et
al., 1996; Hakvoort et al., 1998; Frenkel and Walker, 2001) allows the
interpreter to accurately determine the near-wellbore formation
resistivity distribution under different environmental conditions.
However, the full benefit of array technology can only be achieved if
multi-dimensional, joint In practice, however, especially when we are dealing with the suite of old electrical logs, the latter are often not properly calibrated and depth matched (due to multiple runs of the logging instruments in the same well). Some important logs (e.g., mud resistivity) may not be available for an interpretation process. Some logs could exhibit measurement offsets. Such a problem could appear, for example, on normal galvanic logs, when the absolute voltages in low-resistive formations exhibit an offset (level shift) due to the physical nature of this type of measurement (Frenkel and Walker, 2001).
Special correcting
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 logs. The method consists of a set of preprocessing
Generally, each preprocessing procedure involves three steps. First, we
generate a reference
It should be noted that, for the preprocessing
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 synthetic logs by doing forward Depth matching is accomplished by shifting each log to the appropriate level of the synthetic log to match the main features of the two curves. This could be achieved with log cross-correlation. In practice, such a depth matching procedure should be executed sequentially, using a set of overlapping and sliding windows along the borehole (Frenkel et al., 1997).
Let us assume that the Rm and the caliper logs are known and
that they are depth-matched. To define the
The log scaling (calibration) factors determined by the above
This procedure is similar to the previous one. The difference is that we
apply
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 logs
that are depth-matched and properly calibrated. Applying the 1-D
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
Case Study 1 - B Russian BKZ Data from Western Siberia
A suite of BKZ logs (L045, L105, L225, L425, and L850) (Wiltgen and
Truman, 1993; Harrison, 1995; Frenkel et al., 1997) from a vertical
well, WS-1, logged in Western Siberia was available for an
Analysis of the raw data indicated that the logs were not properly
depth-matched, and that estimation of mud resistivity was required.
Application of the preprocessing
The 2-D
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) 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
It was then possible to simulate all the theoretical curves, including
the first differences and voltages, using the final To investigate this problem, let us consider a 3-layer synthetic formation model presented in Figure 3. It will allow us to illustrate the effect of the reference electrode V4 offset on the SFR curves. This model was generated using a simplified formation model from well E-1. 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
synthetic V4 log, more closely support the results of the rigorous 2-D
A model-based method that allows for fast logging data consistency improvement by means of log data depth matching, recalibrating abnormal logs, and supplying missing log information has been developed and tested. The field tests were performed with the old electrical logs from Siberia and with modern array lateral logs from Australia.
Frenkel, M.A., et al., 1996, Rapid well-site
Frenkel, M.A., Mezzatesta, A.G., and Strack, K.-M., 1997,
Enhanced interpretation of Russian and old electrical resistivity logs
using Frenkel, M.A., and Walker, M.J., 2001, Impact of array lateral logs on saturation estimations in two exploration wells from Australia: Paper FFF, presented at the SPWLA ALS, Houston.
Hakvoort, R.G., et al., 1998, Field measurements and
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 logs for well
WS-1.
Figure 2. Raw logs and 
Figure 3. Effect of the reference voltage V4 offset on the SFR curves.
Model parameters: Rm=0.06 