Step 3. Compile existing data

Recommendations: Acquisition/Organizing/Digitizing Existing Geo-Data

The defined objective is the integration of AEM data with other available data to build a hydrogeologic conceptual model (HCM). The existing data serve two purposes: 1) to support the acquisition and interpretation of the AEM data and 2) to provide additional information used in building the HCM.

Determining the amount of existing data needed relies on local knowledge of the hydrogeologic system but, in general, the more high-quality data the better. Well data (lithology, screened intervals, water levels, specific capacity tests, water chemistry and borehole geophysical logs), geological and hydrogeological interpretations are essential for the design and interpretation of an AEM survey. Given that well data will eventually be needed for the interpretation and building of the HCM, the procedure should be to get the well data organized and digitized so they can be used in the design of the survey.

When gathering existing data, the local water agency might decide to quality screen and record all available data in the area, documenting why certain data are omitted on the basis of poor or unusable quality. This is likely to be a very wise long-term investment as it avoids having to revisit old well completion reports and other datasets and reports at some later time. The data will all be stored and maintained in a robust open database structure.

For budgetary and time constraints, selecting certain wells to develop a representative dataset can be a cost- and time-effective alternative. When doing so, it is important to set up a selection and quality screening process to ensure that the selected wells represent as large a portion of the area of interest (for AEM data acquisition and/or HCM development) as possible; in areas with a high degree of spatial heterogeneity there is likely a need for a higher density of wells. When working with the selected dataset, we recommend capturing and quality screening all available information from the wells.

In the following sections, we describe the processes that Stanford researchers have adopted and developed for reviewing and compiling the well data needed to acquire and interpret AEM data.

Lithology Logs (also referred to as Geologic Logs and Well Logs)

We first address the need for lithology logs, which provide essential information about sediment texture/type and rock type in the subsurface. The experience of Stanford researchers, in the acquisition of data in the Central Valley of California, in Butte and Glenn Counties and in the Kaweah subbasin, suggests that for AEM flight lines spaced up to 2 km apart, we should aim for two high-quality lithology logs per section. (A section is 1 mile x 1 mile or 1.6 km x 1.6 km.) We have found that this provides adequate lithologic data for 1) the initial modeling (to estimate the depth of imaging and resolution of the AEM data), 2) flight line planning, and 3) the rock physics transform (to transform the resistivity models to lithology). In developing the rock physics transform, co-located lithology and AEM data are required, so the flight lines need to be located within ~50 m of the high-quality lithology logs. If the line spacing is greater than 2 km, we should aim for two high-quality logs per section, in the sections containing AEM flight lines. The location of the lithology logs should be such that the AEM lines can be moved to go over a number of them, so as to have the desired co-located data.

Various procedures have been adopted or developed by researchers at Stanford University for identifying, locating, digitizing, and categorizing lithology logs. We first identify high quality lithology logs in the study area, defining high quality as a lithology log that is relatively deep, given the depth of wells in the area, with relatively fine depth discretization in the lithology descriptions, and can be confidently located to within 20 m. We then digitize the logs and categorize the lithology descriptions. Table 4.1 summarizes the work required to go through the complete process to obtain two high quality lithology logs per section for 50 sections, i.e. 100 lithology logs; total time 12 to 38 hours, so .25 to .36 hours for each high-quality lithology log that is required. The developed procedures are described in the documents linked below.
 

Table 4.1. Time required to identity, locate and digitize high quality lithology logs.

• Procedure for Identifying High Quality Lithology Logs
• Procedure for Digitizing Lithology Information
• Procedure for Categorizing Lithology Information

Geophysical Logs

While there are many types of geophysical logs we focus on normal-resistivity logs. These are the most common type of log acquired in water wells, and provide information about the geophysical property of the subsurface measured with AEM -  the electrical resistivity. The resistivity log measurements of resistivity can greatly improve the accuracy of the initial modeling performed prior to data acquisition and are also used in the interpretation of the AEM data.

Resistivity logs are rarely acquired in private water wells used for domestic purposes, are sometimes acquired in private agricultural irrigation wells, and are often acquired in private wells providing a municipal supply. They are now commonly acquired when monitoring wells are installed by a state or local government agency. Oil and gas wells generally have resistivity logs but the logs may not start until much deeper than the portion of the subsurface of interest in an AEM project. As such it can be challenging to obtain shallow resistivity logs  for a given survey area. Thus, our recommendation for the number of resistivity logs to obtain at least ten resistivity logs that contain information over the expected depth interval of the AEM measurements and that are spatially distributed over the survey area. This number of resistivity logs will provide sufficient information to inform both the initial modelling and the approach taken to transform resistivity measurements to lithology. Procedures have been developed by researchers at Stanford University for the purposes of obtaining high-quality resistivity logs.

• Procedure for Obtaining High Quality Normal-Resistivity Logs
• Digitizing Normal-Resistivity Logs

Recommendations: Compiling Other Data/Information

In addition to compiling the geo-data, there are other data and information important for the design of the AEM survey, and data acquisition and interpretation. These are described below.

1. Infrastructure information, both type and location, is important for flight line planning and post-data-acquisition processing and inversion. This information includes power lines both above ground and underground, metallic pipelines, railways, highways, confined animal feeding operations, dwellings, towns, canals, and any other known infrastructure that affects the AEM data collection.
2. Any areas with flight restrictions, e.g. military or environmental protection areas or other areas as designated by the Federal Aviation Authority.
3. Airports and logistical support locations including internet access, wireless services, fuel supply, hospitals and law enforcement availability.
4. Location of fire and rescue facilities and services.