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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).
uModel-based correction/restoration
uModel-based correction/restoration
uModel-based correction/restoration
uModel-based correction/restoration
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uModel-based correction/restoration
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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
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
Special correcting procedures must be applied to prepare the raw data
for joint, inversion-based interpretation. Once the logs pass through
these procedures, they can be used to accurately determine formation
resistivity, and then to perform more reliable petrophysical
A
Model-based
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
procedures, designed to perform
Generally, each preprocessing procedure involves three steps. First, we
generate a reference earth model using the accurate 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
Depth matching is accomplished by shifting each
Let us assume that the Rm and the caliper logs are known and that they are depth-matched. To define the earth model structure, we apply radial, one-dimensional (1-D), or point-by-point inversion to a set of logs, including the poorly calibrated ones. We apply this inversion over a short interval within a relatively thick and uninvaded formation (Frenkel et al., 1997).
The
This procedure is similar to the previous one. The difference is that we
apply inversion to logs that do not exhibit measurement offset. To
determine an offset value, it is sufficient to run the inversion
algorithm over a short interval. Then, one calculates all the synthetic
logs using the generated formation model. The
Missing
This procedure is quite similar to the offset correction approach. As an
example, we show here how to restore the mud 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 logs (L045, L105, L225, L425, and L850) (Wiltgen and
Truman, 1993; Harrison, 1995; Frenkel et al., 1997) from a vertical
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
Case Study 2 B - Array Lateral
This section covers the array lateral
At a 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
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 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
A model-based method that allows for fast logging data consistency
improvement by means of
Frenkel, M.A., et al., 1996, Rapid Frenkel, M.A., Mezzatesta, A.G., and Strack, K.-M., 1997, Enhanced interpretation of Russian and old electrical resistivity logs using modeling and inversion methods: Paper SPE 38688, presented at the SPE ATCE, San Antonio. 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
inversion results of the high-definition lateral Harrison, B., ed., 1995, Russian-style formation evaluation: LPS, London.
Hilchie, D.W., 1979, Old electrical
SPWLA, Houston Chapter, 1979, The art of ancient
Wiltgen, N.A., and Truman, R.B., 1993, Russian lateral (BKZ)
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. |