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Research Papers: Petroleum Engineering

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

[+] Author and Article Information
Yanfang Wang

University of Louisiana at Lafayette,
Lafayette, LA 70504
e-mail: wyfhope@hotmail.com

Saeed Salehi

Assistant Professor
University of Louisiana at Lafayette,
Lafayette, LA 70504
e-mail: sxs9435@louisiana.edu

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received December 5, 2014; final manuscript received May 26, 2015; published online July 7, 2015. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 137(6), 062903 (Nov 01, 2015) (9 pages) Paper No: JERT-14-1400; doi: 10.1115/1.4030847 History: Received December 05, 2014; Revised May 26, 2015; Online July 07, 2015

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

Copyright © 2015 by ASME
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References

Figures

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Fig. 1

Artificial neuron or processing neuron

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Fig. 2

MSE versus depth from well 3

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Fig. 3

UCS versus depth from well 3

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Fig. 4

Summary of model errors. Generation represents the number of hidden neurons.

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Fig. 5

ANN architecture diagram. The optimal network size is the one which has 11 hidden neurons.

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Fig. 6

Performance plot of the developed optimal ANN. Epoch 80 means 80 iterations. MSE of validation error has the lowest value after 80 iterations.

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Fig. 7

Error histogram of the developed optimal ANN

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Fig. 8

Regression plot of the developed optimal ANN

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Fig. 9

Ranking of model input, one single input channel. This figure indicates that when depth is the input, the ANN gives the lowest MSE, which means depth is the leading factor of this model.

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Fig. 10

Ranking of model inputs. MSE has rapidly decreased when the parameters have been added as inputs.

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Fig. 11

Ranking of model inputs from the model of Fruhwirth et al.

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Fig. 12

Simulation cross-plot for well 1 (actual pump pressure versus predicted pump pressure). The MSE error line is very close to the zero line.

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Fig. 13

Simulation cross-plot for well 2 (actual pump pressure versus predicted pump pressure). The MSE error line is very close to the zero line.

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Fig. 14

Simulation cross-plot for well 3 (actual pump pressure versus predicted pump pressure). The MSE error line is very close to the zero line.

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Fig. 15

The overall simulation results for well 1, well 2, and well 3

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