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Talks and White Papers

This page contains downloadable presentations, white papers, and other resources for our clients and friends. Click the item to open or download it.

Better Data for Better Site Conceptual Models

WhitePagesPP1Some site remediation projects have been very successful, while others have not. One of the biggest factors in remediation project success is effective use of site conceptual models. Better use of these models can significantly increase the chance of a positive outcome. For many projects, the process is very linear: investigation, feasibility study, remedy selection, and implementation. Then, if the remedy doesn’t work, try another approach until something finally works. Often the investigation phase includes a site conceptual model. Usually the model is based on regional geology with some input from surface geology at the site, perhaps supplemented by well data. Many times, once the model has been created, it is used throughout the life of the project as it was originally defined.

Two changes can greatly improve the use of the site conceptual model, leading to a more positive outcome.

The first is to more thoroughly involve input from geologists in creating the original model. The flow of groundwater, contaminants, and reactants is very complex, involving interactions between one or more liquid phases, various parts of the solid matrix, other constituents like organic matter and manmade materials, along with contaminants and reagents. Practitioners should gather enough geologic information to adequately characterize all of these materials, recognizing that many interactions operate at a micro, rather than a macro scale.

WhitePagesPP2Secondly, the site conceptual model should be a dynamic, rather than a static document. Throughout the project additional information is often gathered about subsurface conditions. This additional information should be integrated into the model on an ongoing basis so that the best decisions can be made at all stages of the project.
By integrating these two changes into accepted best industry practices and government guidance documents, and managing projects this way, we can greatly increase the number of successfully completed projects.

 

 

 

 

Gathering, Managing, and Displaying Site Environmental Data Using Automated Tools

WhitePagesPP3The requirements for managing environmental data for environmental investigation and remediation projects remains a challenge, but automated tools can help. While in the past the main tool for analyzing was laboratory analysis, and that remains important, field observations of various types are increasing in importance. Field techniques were used in the past primarily for screening; however their low cost relative to lab analyses has caused their role in environmental studies to continue to increase.

Automated tools for data management and mapping can be a significant contributor to investigation and remediation project success. This starts with planning your sample events, whether for field sensing or laboratory analyses. Rapid automated analysis can allow flexibility in the field so that the maximum information can be obtained in the minimum amount of time. Commercial data management tools have had to evolve to better handle the increasing importance of field sensing data over depth or time. For lab data, software tools can automate preparation of field materials (container labels, chains of custody, and files for field data entry). Laboratories are now good at providing useful electronic data deliverables, but these files must be checked for accuracy and consistency as part of the data import process, or as a separate data validation step. Environmental data has a lot of issues, such as duplicates at the sample and analysis levels, non-detected and other flagged data, extracted data, analytes measured by different, incompatible methods, and so on that must be handled correctly from import to output in order to generate useful results, so that questions can be answered and reliable decisions can be made based on the data.

Geology Rising - Making Better Site Conceptual Models

WhitePagesPP4

Some site remediation projects have been very successful, while others have not. One of the biggest factors in remediation project success is effective use of site conceptual models. Better use of these models can significantly increase the chance of a positive outcome. For many projects, the process is very linear: investigation, feasibility study, remedy selection, and implementation. Then, if the remedy doesn’t work, try another approach until something finally works. Often the investigation phase includes a site conceptual model. Usually the model is based on regional geology with some input from surface geology at the site, perhaps supplemented by well data. Many times, once the model has been created, it is used throughout the life of the project as it was originally defined.

Two changes can greatly improve the use of the site conceptual model, leading to a more positive outcome. The first is to more thoroughly involve input from geologists in creating the original model. The flow of groundwater, contaminants, and reactants is very complex, involving interactions between one or more liquid phases, various parts of the solid matrix, other constituents like organic matter and manmade materials, along with contaminants and reagents. Practitioners should gather enough geologic information to adequately characterize all of these materials, recognizing that many interactions operate at a micro, rather than a macro scale.

Secondly, the site conceptual model should be a dynamic, rather than a static document. Throughout the project additional information is often gathered about subsurface conditions. This additional information should be integrated into the model on an ongoing basis so that the best decisions can be made at all stages of the project.
By integrating these two changes into accepted best industry practices and government guidance documents, and managing projects this way, we can greatly increase the number of successfully completed projects.

Data Management for the New and Expected Petroleum Baseline Sampling Rules

WhitePagesPP5In February, 2013, the Colorado Oil and Gas Conservation Commission (COGCC) published Rules 609 and 318A. The rules made Colorado the first state in the country to require pre- and post-drilling sampling of water sources near new oil and gas wells permitted after May 1, 2013. A number of oil and gas operators predict that requirements similar to these will be implemented by other states in the near future. This talk will discuss the new Colorado rules and the data management requirements for the sampling. For example, all laboratory results must be uploaded to the COGCC website in one of their specified formats, to be made available to the public. Integrating these requirements into a comprehensive data management process lets project staff perform in-house quality control, reporting, and mapping, and then upload the data to COGCC, properly handling data details like Facility IDs and Sample IDs generated by the state, and allowing easy comparison of pre- and post-drilling samples. As with any other petroleum-related data, the process needs to take into consideration issues like QC samples, reanalyses, and non-detected results, comparison to regulatory limits, and so on, but effective automation of the process can streamline project work and minimize errors.

 

 

Managing Environmental Data for Conceptual Site Models

WhitePagesPP6The requirements for managing environmental data for soil investigation and remediation projects remains a challenge, but automated tools can help. While in the past the main tool for analyzing this data was laboratory analysis, and that remains important, field observations of various types are increasing in importance. Field techniques were used in the past primarily for screening; however their low cost relative to lab analyses has caused their role in soil studies to continue to increase.

Automated tools for data management and mapping can be a significant contributor to site investigation and remediation project success. This starts with planning your sample events, whether for field sensing or laboratory analyses. Rapid automated analysis can allow flexibility in the field so that the maximum information can be obtained in the minimum amount of time. Commercial data management tools have had to evolve to better handle the increasing importance of field sensing data over depth or time. For lab data, software tools can automate preparation of field materials (container labels, chains of custody, and files for field data entry). Laboratories are now good at providing useful electronic data deliverables, but these files must be checked for accuracy and consistency as part of the data import process, or as a separate data validation step. Soil and water data has a lot of issues, such as duplicates at the sample and analysis levels, non-detected and other flagged data, extracted data, analytes measured by different, incompatible methods, and so on, that must be handled correctly from import to output in order to generate useful results, so that questions can be answered and reliable decisions can be made based on the data.

Another important issue is proper data modeling and presentation. Traditional conceptual site models may not provide adequate geologic and hydrogeologic guidance for effective high resolution modeling that accurately characterizes the rock and fluid properties, and contaminate distribution. High resolution field methods such as direct push downhole sensing of physical and chemical properties, and other tools, can be a big contributor to better understanding and addressing site conditions. A good data management and display system will enable you to efficiently integrate high resolution data with traditional lab and field data to maximize the return on your investigation and remediation budgets.

Free Environmental Computing Glossary

WhitePagesPP7Geotech is pleased to provide our clients with this useful reference of environmental computing terms. We encourge your feedback, especially if you would like to suggest any corrections.