Quality control of sub-surface and wellbore position data

Abstract

There is provided a method of assessing the quality of subsurface position data and wellbore position data, comprising: providing a subsurface position model of a region of the earth including the subsurface position data, wherein each point in the subsurface position model has a quantified positional uncertainty represented through a probability distribution; providing a wellbore position model including the wellbore position data obtained from well-picks from wells in the region, each well-pick corresponding with a geological feature determined by a measurement taken in a well, wherein each point in the wellbore position model has a quantified positional uncertainty represented through a probability distribution; identifying common points, each of which comprises a point in the subsurface position model which corresponds to a well-pick of the wellbore position data; deriving for each common point a local test value representing positional uncertainty: selecting some but not all of the common points and deriving a test value from the local test values of the selected common points; providing a positional error test limit for the selected common points; and comparing the test value with the test limit to provide an assessment of data quality.

Description

Technical field [0001] The invention relates to a method for evaluating the quality of underground location data and wellbore location data. Background technique [0002] This document aims to highlight the main differences between the methods of data quality assurance that exist in patent applications and the prior art implemented as part of commercial software or publishing. [0003] In any problem of predicting unknown quantities by means of other (explainable) quantities that are known or measured, it is very important to pay special attention to the calibration between two sets of variables. In many cases, this calibration is achieved through statistical methods (such as least squares regression) with the aid of a batch of experimental data (training set) with predictor variables and explanatory variables. Ideally, the data values ​​from the training set should be sufficiently scattered and related to the functional relationship in a clear way so that the predictor variable c...

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This information can then be used to determine the multi-level initial trend model, resulting in an adjusted trend model and an adjusted trend uncertainty

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