An unconventional production data analysis technology that uses on gravity and magnetic data was applied to the Eagle Ford formation. The prediction technology uses a neural network with multivariate input and multivariate output and is based on an evolutionary algorithm for neural network teaching. Simultaneously, multivariate neural network output allows for predicting several parameters, such as oil, gas, and water production rates. This prediction is based on multivariate Gaussian distribution theory and an objective function, in this case, Mahalanobis distance versus square distance for one-parameter prediction. In addition, we applied gravity and magnetic depth decomposition technology based on potential field inverse theory.
We propose to use 3D orthogonal decomposition of the seismic cube flattened along the target layer to detect fractures and subtle faults or other latent features under strong noise conditions. The technology is based on principal component analysis (PCA) using computation of eigenvalues and eigenvectors of the 3D autocorrelation function of the original seismic cube. Each orthogonal component is also a cube, and their sum is very close to that of the original cube. Orthogonality means the correlation coefficient between any two components will be about zero. Since the noise and acquisition footprints have no correlation with fractures or faults, reflections or other latent features, they stand out as separate orthogonal components. Wellbore information is usually required to select an orthogonal component useful for fracture detection. Fault and fracture auto tracking technology such as Ant-Tracking (Pedersen at al., 2002) can be applied to the selected orthogonal cube to improve the fracture image.
Seismic inversion requires two main operations relative to changes in the frequency spectrum. The first operation is deconvolution, used to increase the high frequency component of the observed seismic data and the second operation is integration of a reflectivity function to decrease the high frequencies and increase low frequencies of the seismic signal. The first operation is very unstable and non-unique for noisy seismic data. The second operation is very sable in high frequencies but has problems in low frequencies due to undefined low frequency data in seismic traces. By performing both of these operations simultaneously the operation will be stable in high frequency area and can be effectively stabilized in low frequency area based on an a priori acoustic impedance power spectrum and use Tikhonov and Arsenin‟s (1979) regularization technique. This approach can be applied to poststack and pre-stack seismic data.