## 6 Examples
### Example 2--Characterization of a Fault Plane from PTA Data

Table 2: Ranges of variables placed on the input (A) and output (B) layers [9]. Figure 10: Comparison of the predicted and actual characteristics of a faulted reservoir. Example 3--Integration of Seismic, Well-Log and Production Data to Design an Infill Drilling Program  
Seismic data, petrophysical data including well logs and core analysis data, and field production history are the principal sources of information used in reservoir characterization. While the use of each of these data sets individually helps in answering several questions about the characterization of the reservoir rock, the integration of these different data resources in a purposeful way opens new vistas so that effective workflows for sweet-spot locations in infill drilling operations can be adopted. This challenging problem becomes even more perplexing when it needs to be addressed in the presence of a complex reservoir architecture. In this section, we will show how this challenging problem is brought to a resolution with the aid of an integrated model built using artificial neural networks [10]. This example, once again, reiterates how an expert system can become a powerful tool in decision-making processes. The oil field focused on in this study is in North America and is a part of the Wilcox formation. Figure 11 shows the reservoir boundaries and locations of 39 existing wells. An extensive seismic survey was carried throughout the reservoir. Table 3 gives a summary of the availability of the types of the well logs and number of wells with production histories. As can be seen in Table 3, a full suite of well logs was not available for each well. Therefore, it would be necessary to generate synthetic well logs not only at every intersection of the seismic lines but also at some actual well locations where certain types of well logs are missing. In this work, the available production data spans over a period of three years. A typical well production history is decomposed into a plateau period and a decline period. For the purpose of reducing the volume of the data, a curve fitting scheme is applied to express the production data collected during the decline period in a hyperbolic-decline form. For each well, a plateau flow rate is calculated as the arithmetical average of the flow rates, which is then used as one of the fitting parameters for the plateau region. The total duration of the plateau period is also recorded. The production rate of the decline period is fitted using a 3-parameter hyperbolic-decline form as shown in Figure 12.  
Figure 11: Reservoir boundaries and existing well locations of the reservoir in Wilcox formation. \begin{table}
\begin{tabular}{l c} \hline
**DATAS** & Number of Axis \\ \hline GAMMA RAY & 18 \\ INDUCTION RESISTIVITY & 35 \\
**INDUCTION CONDUCTIVITY** & 29 \\ SHORT NORMAL RESISTIVITY & 24 \\
**SPONTANEOUS POTENTIAL** & 27 \\ HISTORICAL PRODUCTION & 25 \\ \hline \end{tabular}
\end{table} 
Table 3: Availability of well logs and historical production data. Following the protocol as described in Figure 12, the production history of a typical well can be simply expressed by five parameters. These parameters consist of two parameters in the plateau region (average flow rate during the plateau region and duration of the plateau time), and three parameters in the decline region (initial flow rate at the beginning of the decline period and the hyperbolic decline constants \(a\) and \(b\)). In this way, in order to predict the expected production at any location within the reservoir, it will be sufficient to find values of these five parameters. In this work, a two-stage tool-development protocol is implemented. This protocol fully utilizes the available field data and provides fast and accurate predictions for the purposes of reservoir characterization and field development optimization. The first tool addresses the need for synthetic well logs at each intersection of seismic lines throughout the field.