Methods for monitoring solids content during drilling operations
A method for monitoring solids content during drilling operations may include collecting real-time cuttings image data at a surface outlet of a natural resource well, determining cuttings characteristics data based on the real-time cuttings image data, collecting real-time surface mud data, and determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model. The cuttings characteristics data may include cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof. The real-time surface mud data may include inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof. Determining real-time, one-dimensional downhole cuttings information may include converting the multi-dimensional computational fluid dynamics model into a one-dimensional continuous cuttings transport model and computing an integrated one-dimensional continuous cuttings transport model. Inputs to the integrated one-dimensional continuous cuttings transport model may include the cuttings characteristics data and the real-time surface mud data.
1. A method for monitoring solids content and modifying mud parameters, drilling operational parameters, or both during drilling operations, the method comprising:
collecting real-time cuttings image data at a surface outlet of a natural resource well;
determining cuttings characteristics data based on the real-time cuttings image data, wherein the cuttings characteristics data comprises cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof;
collecting real-time surface mud data, wherein the real-time surface mud data comprises inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof;
determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model, wherein the determining of the real-time, one-dimensional downhole cuttings information comprises
converting the multi-dimensional computational fluid dynamics model into a one-dimensional continuous cuttings transport model, and
computing an integrated one-dimensional continuous cuttings transport model from the one-dimensional continuous cuttings transport model, wherein inputs to the integrated one-dimensional continuous cuttings transport model comprise the cuttings characteristics data and the real-time surface mud data; and
modifying the mud parameters, the drilling operational parameters, or both in response to the integrated one-dimensional continuous cuttings transport model, wherein:
modifying the drilling operational parameters comprises increasing or decreasing drill pipe revolutions per time, increasing or decreasing a weight on bit, increasing or decreasing a rate of penetration, or combinations thereof, and
modifying the mud parameters comprises increasing or decreasing a viscosity of the drilling fluid, thereby altering the mud rheology, increasing or decreasing a mud density, or both.
2. The method of claim 1 , wherein the real-time cuttings image data is video data.
3. The method of claim 1 , wherein the real-time cuttings image data is collected at a shale shaker.
4. The method of claim 1 , wherein the cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, and cuttings area are determined based on an image processing technique of the real-time cuttings image data.
5. The method of claim 1 , wherein the inlet mud parameters comprise mud rheology, mud density, standpipe pressure, in-flow rate, pump stroke count, pump stroke rates, or combinations thereof.
6. The method of claim 1 , wherein the drilling operational parameters comprise drill pipe revolutions per time, rate of penetration, weight on bit, or combinations thereof.
7. The method of claim 1 , wherein the well planning parameters comprise borehole geometry, borehole survey data, drill bit parameters, or combinations thereof.
8. The method of claim 1 , wherein the multi-dimensional computational fluid dynamics model is two-dimensional or three-dimensional.
9. The method of claim 1 , wherein converting the multi-dimensional computational fluid dynamics model into the one-dimensional continuous cuttings transport model comprises:
choosing a multi-dimensional computational fluid dynamics model type from the group of Direct Numerical Simulation, Large Eddy Simulation, and Reynolds Averaged Navier-Stokes Simulation;
choosing a dual-phase modeling method from an Eulerian-Eulerian or Eulerian-Lagrange method;
determining lab flow loop measurements;
inputting the lab flow loop measurements as a boundary condition in the multi-dimensional computational fluid dynamics model;
inputting the field experiment data as a boundary condition in the multi-dimensional computational fluid dynamics model; and
reducing the multi-dimensional computational fluid dynamics model to the one-dimensional continuous cuttings transport model using section integration.
10. The method of claim 1 , wherein computing the integrated one-dimensional continuous cuttings transport model comprises:
inputting cuttings characteristic data into the one-dimensional continuous cuttings transport model;
inputting real-time surface mud data into the one-dimensional continuous cuttings transport model;
computing outputs of the one-dimensional continuous cuttings transport model;
determining the integrated one-dimensional continuous cuttings transport model using a data assimilation method on the one-dimensional continuous cuttings transport model; and
generating outputs for the integrated one-dimensional continuous cuttings transport model.
11. The method of claim 10 , wherein the data assimilation method is a filtering algorithm.
12. The method of claim 11 , wherein the filtering algorithm is a particle filtering technique.
13. The method of claim 11 , wherein the filtering algorithm is a Bayesian technique.
14. The method of claim 11 , wherein the filtering algorithm is a Kalman-filtering technique.
15. The method of claim 11 , wherein the filtering algorithm is an Ensemble-Kalman filtering technique.
16. A method for monitoring solids content and modifying mud parameters, drilling operational parameters, or both during drilling operations, the method comprising:
collecting real-time cuttings image data at a surface outlet of a natural resource well;
determining cuttings characteristics data based on the real-time cuttings image data using an image processing technique of the real-time cuttings image data, wherein the cuttings characteristics data comprises cuttings size distribution, cuttings volume, cuttings velocity, cuttings orientation, cuttings area, or combinations thereof;
collecting real-time surface mud data, wherein the real-time surface mud data comprises inlet mud parameters, drilling operational parameters, well planning parameters, or combinations thereof;
determining real-time, one-dimensional downhole cuttings information based on a multi-dimensional computational fluid dynamics model by converting the multi-dimensional computational fluid dynamics model into a one-dimensional continuous cuttings transport model, and computing an integrated one-dimensional continuous cuttings transport model, wherein
inputs to the integrated one-dimensional continuous cuttings transport model comprise the cuttings characteristics data and the real-time surface mud data,
the conversion comprises
choosing a multi-dimensional computational fluid dynamics model type from the group of Direct Numerical Simulation, Large Eddy Simulation, and Reynolds Averaged Navier-Stokes Simulation,
choosing a dual-phase modeling method from an Eulerian-Eulerian or Eulerian-Lagrange method,
determining lab flow loop measurements,
inputting the lab flow loop measurements as a boundary condition in the multi-dimensional computational fluid dynamics model,
inputting the field experiment data as a boundary condition in the multi-dimensional computational fluid dynamics model, and
reducing the multi-dimensional computational fluid dynamics model to the one-dimensional continuous cuttings transport model using section integration, and
the integrated one-dimensional continuous cuttings transport model is computed by inputting cuttings characteristic data into the one-dimensional continuous cuttings transport model,
inputting real-time surface mud data into the one-dimensional continuous cuttings transport model,
computing outputs of the one-dimensional continuous cuttings transport model,
determining the integrated one-dimensional continuous cuttings transport model using a data assimilation method on the one-dimensional continuous cuttings transport model, and
generating outputs for the integrated one-dimensional continuous cuttings transport model; and
modifying the mud parameters, the drilling operational parameters, or both in response to the integrated one-dimensional continuous cuttings transport model, wherein:
modifying the drilling operational parameters comprises increasing or decreasing drill pipe revolutions per time, increasing or decreasing a weight on bit, increasing or decreasing a rate of penetration, or combinations thereof, and
modifying the mud parameters comprises increasing or decreasing a viscosity of the drilling fluid, thereby altering the mud rheology, increasing or decreasing a mud density, or both.