The reliability problem related to directional survey data

Abstract

The validity of error model predictions of wellbore position accuracy is highly dependent on the application of rigorous quality control procedures to the survey data. Concern has been expressed within the SPE Wellbore Positioning Technical Section (WPTS, formerly ISCWSA) that failure to apply the necessary operational procedures may be commonplace, raising questions about the reliability of the survey data so generated. Directional survey data that does not conform to its model’s predictions represents a risk in terms of lost production, damage to infrastructure and loss of life.

This paper lists all sources of error, describes internal data checks that are capable of identifying many of them, and highlights those that are missed and which will therefore require alternative QC measures. Real wellbore survey data are used to illustrate how the use of inadequate QC procedures can lead to invalid survey data being accepted as valid.

The paper is the product of collaborative work within the SPE WPTS.

Introduction

Like all measurements, downhole directional surveys are subject to error. Downhole surveys are carried out remotely, without the closure and correction normally associated with survey measurements on surface, and the resulting errors can be significant with respect to the positional objectives for a well. Planners therefore require an estimate of the position uncertainty associated with any proposed survey programme. Such estimates are provided by survey tool error models, also known as instrument performance models.

Position uncertainty estimates are used to determine if there is an adequate probability of hitting the geological target, of avoiding collision with offset wells, and of drilling a successful relief well in the event of a blow out. These are high value decisions and they depend heavily on the validity of the uncertainty estimates. The WPTS has made substantial efforts to improve and standardise survey tool error modelling. Models have been published that accommodate nearly every type of survey tool, their response to varying environmental effects and the application of advanced correction techniques1, 2. However, error models are based on many assumptions about tool quality, operating procedures and environmental conditions. If the actual survey data are not acquired in conformance with the model’s assumptions, the uncertainty estimate is invalid and it can no longer be assumed that the directional objectives for a well are being met. It is therefore necessary to ensure, to the greatest possible extent, that the survey data are reliable.

It is apparent to those working in the wellbore positioning discipline that the degree to which surveys are validated against their model varies greatly and that the critical importance of this activity is not widely understood. In fact there is concern that the recent provision of a “better” error model might have exacerbated the situation, since the apparent logic of some users is that the quality of the error model determines the quality of the survey data3. It is now realised that the provision of an advanced error model without an accompanying set of validating QC measures represents a dangerously incomplete solution. A comprehensive set of QC measures, derived from the model, is required.

Various methods of QC are possible. Downhole survey tools measure their attitude with respect to the Earth’s gravity field and its magnetic or spin field. A survey tool’s error model predicts how well the measured field values should agree with the theoretical fields. Compliance of the survey data with these criteria indicate compliance with the inclination and azimuth accuracy assumptions of the model. When applied to individual stations this method is very cost effective, since it requires no significant additional data acquisition time and no additional processing effort. However, the reliability of this test is very dependent on the wellbore and tool attitudes. This limitation can to some degree be overcome with multi-station analysis of reference field measurements, but these techniques normally require specialist supervision and therefore incur some additional cost.