There is one main reason to collect data: to make decisions. Testing a target quickly is the goal of any exploration group and killing a project can be one of the most important decisions a company can make.

“A 99% failure rate or .01% discovery rate means that, ultimately, what we’re trying to do in the industry is we’re trying to kill things,” said Neville Panizza, Exploration Data Systems Design & Implementation Manager of First Quantum Minerals (FQM).

Real-time data collection reduces the time to decision points and ultimately discovery. The faster scenarios are approved\disproved, the less the overall cost and the faster a project can be reviewed.

While geoscientists are collecting more data than ever before, it is imperative that all data is available at the time of decision and this is where system integrations (APIs) come in.

“We are trying to deliver as much information as possible through our systems, so this data is available at a decision point.”

Turn data into knowledge

FQM Exploration are creating a technology ecosystem that allows them to make decisions in near real time. Drill rig data, geological models, and other test data, all come together as they are collected.

“Basically, data must be delivered at the time a decision is required… If you’re getting data after the decision point, then it’s just data. But, if you’re getting it at a decision point, then it becomes knowledge.”

Their team is using Seequent Central to integrate data from multiple sources and software so that geoscientists can do what they do best — analyse it, improve their model, and take action.

“Integrated data systems, providing quality data, is a foundation to the discovery process.”

Neville presented at Lyceum Perth, Seequent’s annual user conference.

Watch his complete talk here:

Neville shares how his team integrates all data at Lyceum Perth.

Decision making vs data collection

With vast amounts of information coming from disparate sources, the exploration decision-making process can be slow. Errors often aren’t caught until after an entire phase of a project is completed. Vital information becomes siloed within teams or users and data simply does not make it to the decision-makers in time.

System managers like Neville are reversing the status quo of this traditional decision-making process.

Typically, post field collection the results are compiled, interpreted, and a decision is made. Since decisions are made after fieldwork is done, in a sense, data collection drives the next decision. And, in some cases, the decision could be to return to the field because of a lack of data.

FQM exploration now use the power of Central to integrate and display field data in near real time — giving insights that drive decisions in the moment. Since decisions can be made in the field, now the decisions can drive what data is collected.

“Rather than completing an entire 10 drillhole program and then realising, ‘Oh, we should have really made a decision to move the program after hole three to the east,’ or even kill the project after hole eight, you can actually make this decision in the field.”

Projects become agile as teams continuously learn new information and can adjust course almost in the moment.

Scenario modelling and collaboration

real-time-data-with-Central

Central allows all geological scenarios to be modeled, displayed, and compared simultaneously.

“As new data is collected and integrated into Central, geoscientists have the ability to adjust a field plan to test decisions rapidly. Which scenarios get tested can be changed based on new knowledge gained by each data integration. Testing could also be used to quickly disprove multiple scenarios as opposed to approving a single favourable scenario,” Neville says.

“Real-time integration systems reduce the time to decision points and ultimately discovery.”

Leapfrog 3D models hosted on cloud-based Central allow FQM exploration to collaborate, track versions, comment, and upload new data to models anytime, from anywhere. This step speeds up insights between members and allows them to test and throw out ideas quickly.

With human and artificial intelligence working together efficiently, projects can be assessed and redirected, cutting time and equipment costs.

The faster fruitless projects are killed, the more resources remain in the search for discovery.