System and method for online automation

Patent metadata

Field	Value
Publication number	US-10108155-B2
Application number	US-201414449407-A
Country	US
Kind code	B2
Filing date	Aug 1, 2014
Priority date	Oct 14, 2008
Publication date	Oct 23, 2018
Grant date	Oct 23, 2018

Abstract

A changepoint detector for modeling data received from at least one sensor in a process in the hydrocarbon industry. The data is segmented into a plurality of segments and for each segment a model is assigned and the data corresponding to the segment fit to that model. A plurality of segmentations are thus provided and these segmentations are evaluated and assigned weights indicative of the fit of the models of the segmentation to the underlying data. The segmentation models are further used to calculate a result that may be input to a process control program.

First claim

The invention claimed is:

1. A method for controlling an automated hydrocarbon industrial process to drill a wellbore through an earth formation having a desired wellbore trajectory and at least one controllable parameter of drill bit rotational speed and weight-on-bit, wherein the automated process is subject to at least one dynamic constraint, the method comprising:
receiving a stream of measurement data indicative of depth-of-cut, weight-on-bit, drill bit rotational speed, azimuth, and inclination from a sensor system comprising at least one sensor configured to measure a property from which a functional relationship between the desired wellbore trajectory and the at least one controllable parameter of drill bit rotational speed and weight-on-bit is determined;
postulating that the stream of measurement data is segmented according to a plurality of possible segmentations each possible segmentation comprising a plurality of segments divided by changepoints, wherein each changepoint is indicative of a change in an operating condition;
fitting one or more portions of the stream of measurement data corresponding to each segment in the plurality of segments in each of the plurality of possible segmentations to a model corresponding to the each segment;
evaluating each of the plurality of possible segmentations by determining how well the model corresponding to the each segment in the plurality of segments in the plurality of possible segmentations fits the one or more portions of the stream of measurement data corresponding to the each segment;
using at least one of a most likely segmentation and the models corresponding to the segments of the most likely segmentation to determine the at least one dynamic constraint and the functional relationship between the desired wellbore trajectory and the at least one controllable parameter of drill bit rotational speed and weight-on-bit, wherein the most likely segmentation comprises a one of the plurality of possible segmentations having a best fit between the models corresponding to the segments in the one of the plurality of possible segmentations and corresponding one or more portions of the stream of measurement data for the one of the plurality of possible segmentations;
using the functional relationship between the desired wellbore trajectory and the at least one dynamic constraint to determine a suggested parameter setting for the at least one controllable parameter of drill bit rotational speed and weight-on-bit, wherein the suggested parameter setting is determined such that according to the determined functional relationship between the at least one controllable parameter of drill bit rotational speed and weight-on-bit and the desired wellbore trajectory an improved performance is achieved using the suggested parameter setting while operating within the at least one dynamic constraint;
controlling the automated process by adjusting the controllable parameter to the suggested parameter setting;
determining operating constraints defining a safe operating envelope as a function of drill bit rotational speed and weight-on-bit;
determining rotational speed and weight-on-bit parameters that provide the desired wellbore trajectory within the safe operating envelope; and
outputting a combination of drill bit rotational speed and weight-on-bit to move the drill bit rotational speed and weight-on-bit toward the rotational speed and weight-on-bit parameters for the desired wellbore trajectory.

2. The method of claim 1 , wherein:
the stream of measurement data for the drilling system is indexed by depth;
the plurality of possible segmentations are determined by segmenting azimuth and inclination data of the stream of measurement data into the plurality of segments each having models associated therewith for computing azimuth and inclination as a function of depth;
weights indicative of how well the models fit the azimuth and inclination data of the stream of measurement data associated with each possible segmentation.

3. The method of claim 2 , further comprising:
determining the at least one dynamic constraint and the functional relationship between the desired wellbore trajectory and the at least one controllable parameter of drill bit rotational speed and weight-on-bit for all active segmentations using the models associated with the segments corresponding to a depth to compute inclination and azimuth values;
using the inclination and azimuth values for the determined at least one dynamic constraint and the functional relationship between the desired wellbore trajectory and the at least one controllable parameter of drill bit rotational speed and weight-on-bit to compute calculate dogleg severity and toolface values;
computing a weighted average dogleg severity and toolface from the calculated dogleg severity and toolface values for each segmentation; and
providing the weighted average dogleg severity and toolface to an automated drill controller.

4. The method of claim 3 , wherein:
each segment is defined by a first changepoint (MD1) and a second changepoint or a current depth location (MD2),
dogleg severity for a particular depth location (MD2) and a particular segmentation p is computed using the formula:
DLS p =A COS(COS( I 2− I 1)−SIN( I 1)*SIN( I 2)*(1.0−COS( A 2− A 1))/( MD 2− MD 1))  (1)

y =COS( A 2− A 1)*SIN( I 2)*SIN( I 1)  (2)

GTF p =A COS(COS( I 1)* y −COS( I 2))/(SIN( I 1)*SIN( A COS( y )))  (3)

where:
I1 and I2 are the inclination values computed at the first changepoint MD1 starting the segment to which the particular depth location MD2 belongs and at the particular depth location MD2 using the inclination model associated with the segment to which the particular depth location MD2 belongs, respectively
A1 and A2 are the azimuth values computed at the first changepoint MD1 starting the segment to which the particular depth location MD2 belongs and at the particular depth location MD2 using the inclination model associated with the segment to which the particular depth location MD2 belongs, respectively
DLS p is the dogleg severity at MD2 computed with the segmentation p
GTF p is the toolface at MD1 computed with the segmentation p; and
the weighted averages for dogleg severity and toolface are computed using the weights associated with each segmentation using the formulas:

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Assignees

• Schlumberger Technology Corp

Inventors

• Aldred Walter
• Dunlop Jonathan
• Belaskie James

Classifications

• E21B7/04

  Directional drilling · CPC title

• E21B2041/0028

  Fixed Constructions · mapped topic

• E21B44/00 Primary

  Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions · CPC title

• G05B13/04 Primary

  involving the use of models or simulators · CPC title

• E21B41/0092

  Fixed Constructions · mapped topic