A Well Curve Inversion Method

Abstract

The invention relates to a well curve inversion method, which comprises (1) selecting wells and well curves in a working area; (2) time-depth conversion of well curves; (3) extracting seismic attributes along well trajectory; (4) well curve Analysis of seismic attribute interaction; (5) Select horizon; (6) Establish stratigraphic framework; (7) Establish initial model; (8) Neural network training; (9) Output training results. The beneficial effects of the present invention are: the present invention uses the RBF radial basis function neural network algorithm to carry out the well curve inversion algorithm, integrates geological statistics and neural network technology well, and realizes multi-attribute-driven, geological statistics-based fractures Density inversion. This method is an improvement over the existing stochastic simulation and multi-attribute well curve inversion. It can not only obtain stable inversion results, but also reflect the nonlinear constraints of seismic multi-attributes on inversion results, and its prediction accuracy is higher than other software that only relies on geological statistics.

Description

technical field [0001] The invention belongs to petroleum exploration and development technology, in particular to a well curve inversion method. Background technique [0002] At present, there are two methods of well curve inversion: random inversion of well curve and neural network inversion of well curve. The stochastic inversion method of well curves adopts the co-simulation method, and uses seismic data as "soft" constraints to carry out well curve inversion. The constraining effect of seismic attributes in the inversion depends on the correlation between seismic attributes and well curves. Larger, the greater the confinement effect of the earthquake. The neural network well curve inversion method utilizes the neural network to establish the non-correlation relationship between the well curve and seismic attributes, and uses the correlation relationship to carry out the well curve inversion. The above two inversion methods have certain disadvantages. The random invers...

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Problems solved by technology

The above two inversion methods have certain disadvantages. The random inversion method of well curves can only introduce a seismic attribute as a constraint. At the same time, the constraint process is a simple linear mapping. If the correlation between seismic information and well curves is complicated, The constraint effect of seismic information on the inversion results is greatly reduced, and the inversion results are basically interpolation results

The neural network algorithm is not stable enough. If the correlation between the seismic attributes and the well curve is not very obvious, the well curve inversion result of the neural network is often not good, and the multi-solution is very strong

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