The Role of Soil Data in Pipeline Integrity

Since the early 20th century, researchers and engineers have been using soils information to address corrosion issues on buried metallic materials. While this area of research was quite active from the 1920’s to the 1950’s, soils related corrosion pipeline research was declining until the mid eighties. For the non-soil scientist this area is complex and disruptive since there is usually no easy equation or consistent answer from buried point to buried point. Historically, the pipeline industry has developed coating systems that effectively place a barrier between the soil environment and the pipe surface. Over time, these coatings can deteriorate to such a level that the environment can either migrate through the coating or come in direct contact with the pipe surface. The second level of protection used in the defense of the pipe from the environment is the cathodic protection (CP) system. These systems can also deteriorate, but they can be monitored through measurement techniques at a given interval. The CP system can provide an adequate level of protection, thus minimizing potential pipeline integrity threats such as external corrosion and stress corrosion cracking. This paper will present a historical overview of the use of soils and soil survey information and will provide an overview our experiences related to the use of data effecting external corrosion and stress corrosion cracking of line pipe steels.

A comparison of gravity prediction methods on actual and simulated data

A comparison was made between Shepard’s method (inverse‐distance weighting) and collocation (linear filtering) for the purpose of predicting gravity anomalies. Tests were made with actual data from southern California and with simulated data created from buried point masses generated by a random number generator. The autocorrelation functions of the simulated and actual gravity data behaved very much alike. In general, the sophisticated collocation method did produce better results and very good variance estimates, compared with Shepard’s method, for simulated data. The advantage was less for actual data. The cost of the better results is the use of more computer time. The most important scientific conclusion of this study is that careful trend removal must be done and an adequate data sample obtained to produce truly optimal results from collocation. The variance estimates are much more sensitive to the form and calibration of the model autocorrelation function than are the prediction results.