PSO drilling parameter optimization method based on neural network

A drilling parameter and neural network technology, applied in the field of drilling engineering, can solve problems such as uncertainty, random nonlinearity, extremely complex calculation process, great practicability and limitations, and achieve strong anti-interference and high-efficiency search capabilities , Versatile effect

Active Publication Date: 2020-02-28
BC P INC CHINA NAT PETROLEUM CORP +1
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Problems solved by technology

However, due to the complexity of the drilling process, the drilling parameters are not only affected by multiple factors, but also there is correlation coupling between each parameter, which presents great uncertainty, randomness and nonlinearity. Op

Method used

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  • PSO drilling parameter optimization method based on neural network
  • PSO drilling parameter optimization method based on neural network
  • PSO drilling parameter optimization method based on neural network

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Embodiment

[0031] 1. Data collection and processing

[0032] With the development of automation and digital technology, many advanced real-time data acquisition and detection platforms have been introduced into the drilling process, accumulating massive drilling data. These data have the characteristics of huge quantity, rich sources, non-uniform data types, and high data redundancy. According to the known experience, the factors affecting the drilling efficiency are complicated. According to the data, the common target parameters affecting the ROP are: well depth, layer, suspension weight, weight on bit, torque, drilling time, displacement, pump pressure, Speed, drilling fluid density, drilling fluid viscosity, azimuth, well deviation, vertical depth, design well depth, etc. Since the designed vertical depth, designed well depth, azimuth and well deviation have been determined before drilling, they can be regarded as static data and not analyzed. According to the analysis, determining...

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Abstract

The invention belongs to the technical field of drilling engineering, and particularly relates to a PSO drilling parameter optimization method based on a neural network, which is characterized by comprising the following steps: (1) inputting current design parameters into a drilling parameter optimization system database; (2) querying whether adjacent well historical data of the current well exists or not, transmitting the data to a system data analysis processing module, inputting the preprocessed data into a neural network for model training, and outputting a pre-drilling parameter prediction model; and (3) after drilling, synchronously obtaining current while-drilling data parameters, calculating the current mechanical drilling speed, updating the model, and comparing the mechanical drilling speed under the same stratum lithology and borehole size in the model with the current mechanical drilling speed. Original parameter signals are collected, information data of the whole drillingprocess are processed and analyzed through software, key data are subsequently optimized, and real-time and dynamic intelligent parameter optimization and system comprehensive analysis are achieved through multi-element technology fusion of the whole drilling parameter optimization process.

Description

technical field [0001] The invention belongs to the technical field of drilling engineering, in particular to a neural network-based PSO drilling parameter optimization method. Background technique [0002] Drilling engineering is a key link in the field of oil and gas development, and one of the links with the largest economic investment and the highest risk in the exploration and development process. The efficiency and cost of the drilling process directly reflect the drilling level, which is of great significance to subsequent oil and gas production and operations. Due to the comprehensive influence of geological conditions, mechanical properties of drilling rigs, drilling fluid properties, bit types and other factors, the drilling process presents great uncertainty. For a long time, how to improve drilling efficiency, shorten drilling cycle and save development cost under the influence of complex factors has become an engineering difficulty and hot spot in drilling work...

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Application Information

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IPC IPC(8): G06F30/25G06F30/27G06N3/00G06N3/04G06Q10/04G06Q50/02
CPCG06N3/006G06Q10/04G06Q50/02G06N3/045
Inventor 郭修成夏泊洢李鹏娜白冬青李永钊匡涛常杨周超张青钟健邓旭庄纯才李果王西贵宋成坤池丽军解志亮李帅岐
Owner BC P INC CHINA NAT PETROLEUM CORP
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