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While-drilling rock mineral component identification method and device based on artificial intelligence

A technology of rock mineral composition and artificial intelligence, which is applied in the fields of earthwork drilling, data processing application, measurement, etc. question

Active Publication Date: 2020-04-07
PETROCHINA CO LTD
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003]Currently, due to the influence of late arrival time, engineering cuttings logging cannot timely and accurately reflect the rock and mineral composition of the drilled formation; There is a certain distance from the drill bit, so it is also unable to reflect the changes in the formation rock mineral composition in time

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  • While-drilling rock mineral component identification method and device based on artificial intelligence
  • While-drilling rock mineral component identification method and device based on artificial intelligence
  • While-drilling rock mineral component identification method and device based on artificial intelligence

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Embodiment 1

[0057] figure 1 A schematic flow chart of the artificial intelligence-based rock mineral component identification method while drilling provided in Embodiment 1 of the present invention, as shown in figure 1 As shown, the method includes:

[0058] 101. Obtain parameter values ​​of input parameters, where the input parameters include engineering parameters while drilling;

[0059] 102. Use the parameter value of the input parameter as the input of the BP neural network, and obtain the rock mineral component identification result while drilling output by the BP neural network, wherein the BP neural network is based on different parameter values ​​of the input parameters and The corresponding identification results of rock mineral components while drilling are sample sets, which are established through deep learning training.

[0060] In practical applications, the executor of the artificial intelligence-based rock mineral composition identification while drilling method may be...

Embodiment 2

[0086] In the second embodiment, input parameters and training parameters determine the identification effect of rock mineral components by the BP neural network. In this embodiment, the input parameters are selected from well depth, drilling pressure, rotational speed, mechanical drilling speed, torque, rock-breaking energy equivalent of drilling pressure, rock-breaking energy equivalent of torque, and total rock-breaking energy equivalent. The main parameters in the training parameters are the number of hidden layers and the learning rate. These two parameters can determine the optimal value through the quality of the rock mineral composition identification results obtained through trial calculation. Preferably, the number of hidden layers in the BP neural network is 6, and the learning rate is 0.90. Specifically, the learning rates are respectively set at 0.75, 0.80, 0.85, 0.90, and 0.95 for training. Through calculation and comparison, when the learning rate is 0.90, the r...

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Abstract

The invention discloses a while-drilling rock mineral component identification method and device based on artificial intelligence. The method comprises the steps: acquiring parameter values of input parameters, wherein the input parameters comprise while-drilling engineering parameters; taking the parameter values of the input parameters as input of a BP neural network, and acquiring while-drilling rock mineral component recognition results output by the BP neural network, wherein the BP neural network is established through deep learning training with the different parameter values of the input parameters and the while-drilling rock mineral component recognition results corresponding to the different parameter values as a sample set. In the scheme of the invention, the while-drilling rockmineral component identification model based on the BP neural network is constructed, and the rock mineral components of the drilled stratum can be timely and accurately obtained by inputting the parameter values of the corresponding while-drilling rock mineral component identification parameters, so the exploration and development of petroleum are facilitated.

Description

technical field [0001] The invention relates to the technical field of petroleum exploration and development, in particular to an artificial intelligence-based method and device for identifying rock mineral components while drilling. Background technique [0002] The traditional identification of rock mineral components is mainly analyzed through the method of engineering cuttings logging. Mud logging technology is the most basic technology in oil and gas exploration and development activities. It is the most timely and direct means to discover and evaluate oil and gas reservoirs. It has the characteristics of timely and diverse underground information acquisition and quick analysis and interpretation. Well logging technology is a method of measuring geophysical parameters by using the electrochemical properties, electrical conductivity, acoustic properties, radioactivity and other geophysical properties of rock formations. [0003] At present, due to the influence of late ...

Claims

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Application Information

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IPC IPC(8): E21B47/01E21B47/00G06Q50/02
CPCE21B47/01E21B47/00G06Q50/02
Inventor 杨沛陈龙何仁清
Owner PETROCHINA CO LTD
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