Automation of Well Correlation and Dynamic Synthesis for Efficient Reserves Estimation in Multi-Layered Oil and Gas Field

The studied field was discovered in 1974 and has been in operation for nearly 50 years. Being deposited within a deltaic environment with enormous multi-layer sand-shale series, the field is vertically divided into dozens of geological layers. Previous reserves estimation method of manually performing dynamic synthesis followed by volumetric calculation per layer basis has become less preferable amid increasing drilling and well intervention activities. Meanwhile, reservoir simulation is also inapplicable for reserves estimation due to the field's subsurface complexity. This paper shares an approach to automate well correlation and dynamic synthesis process by integrating static and dynamic data into Visual Basic for Application (VBA) based tool in order to efficiently estimate reserves and accelerate candidate selection for new well drilling and well intervention. Performing dynamic synthesis on a certain reservoir within a well of interest involves estimation of latest fluid status, pressure, water risks, recovery factor, and drainage radius by analyzing recent static and dynamic data from surrounding wells. As the static data and dynamic data from hundreds of existing wells are available in separate databases, the study commences with collecting, updating, filtering, organizing and integrating data into one reliable database. Afterwards, the automation tool is designed to quantitatively mimic the logics of performing well correlation and dynamic synthesis using weighting factors that characterize the reliability of data based on 3 parameters: distance to the well of interest, recentness of data, and sand similarity. Since these parameters have distinctive influence depending on the dynamic property being estimated, influence factors are introduced for each parameter and each dynamic property through trial & error process. Combining weighting and influence factors with available data results in the estimated dynamic properties that become input to volumetric calculation of reserves. In order to validate the model and tool, blind tests are carried out using data from recently drilled wells which are not included in generating the estimation. Pressure blind test shows good correlation between predicted and realized values, meaning that the tool is able to predict pressure accurately. Reserves estimation blind test also shows satisfying results both at reservoir and well level. Following successful blind tests, the tool has been utilized to aid engineers in proposing new wells and well intervention candidates. As a result, 8 wells were able to be proposed in a timely manner for the sanction of future development. This paper presents an efficient, novel and robust approach in estimating reserves for heterogeneous fields where reservoir simulation is inapplicable. The tool also allows straightforward update when adding data from new wells. However, further study is required for estimation in less dense areas where the amount of surrounding wells and data are insufficient.