Rock physical elastic parameter forward modeling method, forward modeling device and electronic equipment
A technology of petrophysical and elastic parameters, applied in the field of oil and gas geophysical exploration, can solve the problems that cannot meet the needs of high-precision seismic interpretation, and achieve the effect of improving the accuracy of forward modeling
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Embodiment 1
[0081] In this embodiment, a deep-water drilling well in a sandstone block in southern China is taken as an example, and the method provided by the present invention is used to carry out petrophysical forward modeling.
[0082] figure 2 A technical flow chart of petrophysical forward modeling according to an embodiment of the present invention is shown.
[0083] image 3 The structural diagram of the deep feedforward neural network is shown. The deep feedforward neural network includes an input layer, an output layer, and two or more hidden layers.
[0084] Figure 4 The depth domain logging data of the drilling well in the example is shown. It can be seen from the figure that the diameter expansion phenomenon occurs in the deep section of the well. Due to the chemical characteristics, the wellbore becomes seriously unstable during deepwater drilling. For this reason, in the embodiment, the deep neural network training is carried out using the well section data with stabl...
Embodiment 2
[0095] Such as Figure 11 As shown, this embodiment provides a forward modeling device for rock physical elastic parameters based on deep learning, including:
[0096] The acquisition unit acquires the depth domain logging data of the research area and forms the training sample set of the deep feedforward neural network;
[0097] The training unit constructs a rock physics forward modeling model based on a deep feed-forward neural network, uses the training sample set for training, and obtains a nonlinear mapping relationship between rock parameters and elastic parameters;
[0098] The forward modeling unit performs rock physical forward modeling of elastic parameters on the target well section based on the rock physical forward modeling model.
[0099] The acquisition unit, the training unit and the forward modeling unit are sequentially connected by communication, the acquisition unit provides the training sample set to the training unit, and the petrophysical forward model...
Embodiment 3
[0101] This embodiment provides an electronic device comprising: a memory storing executable instructions; a processor running the executable instructions in the memory to realize the above-mentioned forward modeling of rock physical elastic parameters based on deep learning method.
[0102] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
[0103] The memory is used to store non-transitory computer readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache). The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like.
[0104] The processor may be a central processing unit (CPU) or other form of proces...
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