# Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study
To compensate for the poor repeatability of the acquisition geometry between the baseline and monitor surveys (different coordinates of the source and receiver, different azimuths, and the number of obstructions), 4D seismic co-processing with field tapes for both the baseline and monitor surveys was used in this study. Deliberate full seismic 4D co-processing starting from the seismic field records is more accurate and robust, and thus far more repeatable, than partial seismic 4D co-processing with Kirchhoff Pre-stack depth migration (KPSDM) or Least Square Pre-stack depth migration (LSM) [12]. Differences in the subsurface cause changes in amplitudes and velocities, resulting in the misalignment of the seismic reflectors. Measuring the misalignment and aligning these surfaces to obtain a reliable differential cube is one of the most important disciplines in 4D seismic processing [13]. One of the most important calibration steps in the time-lapse seismic workflow is the calculation of the optimal dynamic time and space shifts required to calibrate multiple monitor surveys with a baseline survey. Dynamic Time Warping is useful for lining up seismic images from different time-lapse periods, especially for determining the time-variant time shifts. The accuracy of Dynamic Time Warping (DTW) in estimating similarity is being investigated for template matching and clustering of seismic traces [14]. The conversion of seismic reflection data into physical properties of the subsurface, such as elastic parameters, is called seismic inversion [15]. These properties can be correlated with porosity, lithology, fluid saturation, and geomechanical properties [16]. Choosing the most accurate and robust analysis domain is particularly important in 4D time-lapse studies [17], so seismic inversion in 4D can be more effective than regular seismic wiggle-trace work in this regard. Time-lapse (4D) seismic inversion aims can be used to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. The goal of seismic inversion is to determine a reservoir model that minimizes the errors between predicted seismic amplitudes and observed seismic amplitudes [18]. A low-frequency anomaly is usually associated with fluid in the reservoir, while a high-frequency anomaly is associated with gas or clay content of the reservoir [19]. The main challenges in 4D seismic analysis are improved 4D vertical resolution (ideally 1-10 m), improved repeatability that enables an application to carbonate reservoirs, and innovative ways to combine 4D seismic data with other reservoir measurements and simulation methods [20]. In this study, we used a hybrid theory and data model to evaluate the results of 4D seismic inversion and predict changes in reservoir rock properties over 20 years, such as density and porosity, from baseline and monitor surveys. We used a software package from Hampson-Russell (Emerge) that analyzes well logs and seismic data by establishing a relationship between logs and seismic data at well locations. Deep learning was tested against a single attribute list of individual features from a nonlinear regression curve method. The network is used in geophysics to quantitatively predict rock properties from seismic data [21]. In supervised learning, we gave the neural network a set of inputs and outputs for a given problem and let it determine the relationship between those inputs and outputs. The drawback of supervised learning is that enough inputs and outputs are needed to adequately train the network [22]. Matching the observed 4D signal with reservoir performance over the entire production span of the baseline survey in 1994, and the monitor seismic survey in 2014, is evidence that 4D seismic co-processing can fill up the gap left by poor repeatability of seismic acquisition. The mechanism of 4D signal generation was explained by the increase in fluid saturation for oil and water with pressure rise over the 20 years of the 4D time-lapse.