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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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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 log) (Frenkel et
al., 1996; Hakvoort et al., 1998; Frenkel and Walker, 2001) allows the
interpreter to accurately determine the near-wellbore formation
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
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
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 procedures, designed to perform log depth matching, recalibration, offset elimination, and recalculation of missing logs. 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 synthetic logs based on the generated earth model. Finally, we perform depth matching, missing log reconstruction, and abnormal log correction. The reconstructed and corrected logs can be used in the further petrophysical interpretation. 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 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 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 log scaling (calibration) factors determined by the above inversion procedure can then be used to recalibrate the logs. This procedure provides the most reliable and stable results when only two logs with similar resolution are used in the correcting process.
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 log offset is calculated as the difference between the raw and the synthetic logs.
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
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 well, WS-1, logged in Western Siberia was available for an inversion-based interpretation. The data were logged at a rate of 6 samples per meter. The caliper and the SP logs were provided. The goal of interpretation was to evaluate the interval below the Bazhenov Shale in the Jurassic sands.
Analysis of the raw data indicated that the logs were not properly
depth-matched, and that estimation of mud
The 2-D inversion was next used to determine a picture of the formation
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
At a well site, the
The SFR
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, 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 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
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 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 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 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 
Figure 2. Raw logs and inversion results for the interval, 470-500 m,
Well E-1 from Australia. Results of 2-D inversion are shown in tracks 3
and 4.
Figure 3. Effect of the reference voltage V4 offset on the SFR curves.
Model parameters: Rm=0.06 