Analysis of a LNAPL recovery system using LDRM in a South Texas facility

Petroleum leakage from storage tanks, underground pipelines during exploration and production facilities is the reason of hydrocarbon migration into the groundwater. Petroleum companies use various LNAPL (Light Non-Aqueous Phase Liquids) recovery techniques to prevent lateral migration of hydrocarbon through the offsite of a facility. A petroleum refinery facility in the Gulf Coast region of South Texas was selected to evaluate ongoing LNAPL recovery system. Three analyses were carried out in this study. First, hydrogeologic conditions were determined using DGP (Diagnostic Gauge Plots). The concept of why ANT (Apparent LNAPL Thickness) is not a good metric to estimate recovery rates was explained based on hydrogeologic conditions of LNAPL. LNAPL and groundwater surface contour maps were built to have information about the direction of flow. All map illustrations were created using ArcGIS software. Well configurations were used to determine hydrogeologic condition in case of lack sufficient data for DGP. Second, LNAPL transmissivity were estimated using API (American Petroleum Institute) LNAPL Transmissivity Workbook. LNAPL condition was required in estimating LNAPL transmissivity values with API workbook, where methods of analysis are dependent of LNAPL condition. Total fluids recovery data were also used to estimate transmissivity values in the study wells. 0.08 ft2/d transmissivity value was arbitrarily chosen to determine the endpoint of recovery. Third, LNAPL recovery rates were predicted using LDRM (LNAPL Distribution and Recovery Model) for 11 recovery wells in the study region. Single phase –water- extraction method was used for LNAPL recovery under atmospheric conditions. Soil and fluid properties along with recovery system data were required for LNAPL recovery estimation. Some of these data were available from the dataset provided by oil company and some of them were estimated using API and Rosetta databases. Soil properties, radius of recovery values, and water production rates were calibrated in order to fit the LDRM recovery and transmissivity results with the actual field data. The modeled recovery rates and transmissivity values were consistent with actual data. Projections for future in a well were made. The model can be used for the endpoint of recovery projections.