Assessment, monitoring and control of drilling operations and/or geological-characteristic assessment

Inactive Publication Date: 2015-08-13
SCHLUMBERGER TECH CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0004]In one embodiment of the present invention, a dimensionality of a full-scale model (e.g., characterizing variables related to cuttings transport, gas migration and/or the like) is reduced, in an embodiment, data from a plurality of geographically distributed (e.g., depth varying) sensors is received, and a surrogate model is used to estimate variables in real-time. Use of the surrogate model may enable, e.g., particle-filtering processes to be employed during the estimation while still allowing f

Problems solved by technology

However, dynamically solving for variables using the full models may be difficult or impossible given a large number of unknown variables.
Even estimation techniques may

Method used

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  • Assessment, monitoring and control of drilling operations and/or geological-characteristic assessment
  • Assessment, monitoring and control of drilling operations and/or geological-characteristic assessment
  • Assessment, monitoring and control of drilling operations and/or geological-characteristic assessment

Examples

Experimental program
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example 1

[0135]In some embodiments, techniques, methods and systems described herein may be applied to estimate and / or predict properties related to cuttings transports. In one example, a model of a dynamic system describing cuttings transport includes the following relationship:

xk+1=ƒ(xk,zk,qk),yk=g(xk,zk,qk),zk+1=zk+noise,  (1)

where xk is the state vector with nx entries as the average cuttings volume along the annulus, zk is the parameter vector with nz entries as the uncertain parameters (e.g., cuttings slip velocities along the annulus), qk is the input to the process (e.g., pump volumetric flow rate and rate of penetration of the bit), and yk is a vector containing pressure measurements at sparse locations along the annulus. Functions ƒ and g may be functions describing the cuttings transport process based on conservation of mass and momentum respectively or other drilling related models (e.g., torque drag model, temperature models, drillstring dynamics model, hydraulics model etc). Th...

example 2

[0139]Cuttings transport process in a vertical wellbore annulus in oilfield drilling are considered. Considering conservation of mass and a simplified version of linear momentum the process is described by a set of partial differential equations in one dimensional domain Ds≡as, {0≦L≦L} as,

∂(ρrxr)∂t+∂(ρrxrvr)∂s=ρrq,∂(p)∂s+ρg+fsh=0,(5)

where s is the distance along the annulus from the surface, xr(s, t) is the volumetric concentration of the cuttings, ρr(s), ρ(s) are the densities of the cuttings and the density of their mixture with mud, respectively. The variable, νr(s, t), is defined as the velocities of the cuttings, q(s, t) as the rate of cuttings volume production (a function of the rate of bit penetration and bit diameter), p(s, t) as the pressure along the annulus and ƒsh is a pressure loss term due to shear stress. Discretizing the domain Ds in n cells around n nodes at depths siεDs, for i=1, . . . n, and applying an upwind finite volume discretization method subject to bounda...

example 3

Particle Filtering with Switching Models

[0150]FIG. 16 illustrates an example of a particle-filtering framework with switching models. The outputs of M evolution models at time tk are denoted by {μk(i,n)}i=1Nn for n=1, . . . , M where M is the number of different models. The outputs of L observation models are denoted by {yk(i,n)}i=1Kn for n=1, . . . , L. At time tk, each model outputs N, particles denoted by {μk(i,n)}i=1Nn for n=1, . . . , M. Each of these models could describe different behaviors of the process (e.g., normal operation, kick event, etc.). The process outputs {yk(i,n)}i=1Kn for n=1, . . . , L can be obtained based on L different observation models, given the particles {μk(i,n)}i=11, . . . , {μk(i,n)}i=1M. These observation models could consider different states of sensors (e.g., working properly, complete failure, working with a bias error). At an instance in time, a number of particles will be generated based on the suitability of each model, which could allow monit...

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Abstract

In monitoring/control of drilling operation a dimensionality of a full-scale model (e.g., characterizing variables related to cuttings transport, gas migration and/or the like) is reduced and data from a plurality of geographically distributed (e.g., depth varying) sensors is received, and a surrogate model is used to estimate variables in real-time. Use of the surrogate model may enable, e.g., particle-filtering processes to be employed during the estimation while still allowing for real-time estimations, avoiding excessive use of reasonable computational resources (e.g., memory and processing speeds) and/or the like. Operating controls or the like may then be set based on the estimated variables. For example, drilling control parameters may be adjusted based on estimated variables to avoid lost circulation, kicks, stuck pipe, and catastrophic events, optimize drilling parameters such as rate of penetration, improve drilling success probabilities and efficiency and/or the like.

Description

BACKGROUND[0001]This disclosure relates in general to assessing drilling procedures and / or geological characteristics via processing of models, such as model reduction and / or use of surrogate models. Not by way of limitation, the disclosure describes, for example, assessments of geological characteristics pertaining, e.g., to oilfield-drilling operations, based on analysis of real-time data from a plurality of distributed sensors.[0002]Having the capability to understand underground properties and characteristics is very valuable. For example, resources such as oil and natural gas are underground, and human populations are highly reliant on these resources. Nevertheless, successful extraction of resources depends on properly identifying extraction sites and effectively and dynamically tailoring extraction techniques to reach and extract the resources. For example, knowing properties (e.g., location and other properties) about a resource may improve an operator's choice of: a drill-s...

Claims

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Application Information

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IPC IPC(8): E21B44/00E21B49/00E21B47/00
CPCE21B44/00E21B49/00E21B47/00G01V1/40G01V11/00G01V2210/66G01V2200/16
Inventor FRANGOS, MICHALISRINGER, MAURICE
Owner SCHLUMBERGER TECH CORP
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