GEO EXPRO’s Post

Very pleased to see GeoExpro publish an insightful article by my long-time friend and colleague, Richard Taylor, on a technology that has been central to my career and the cornerstone of my business: seismic vectorising. Despite being around for over 30 years, seismic vectorising remains underappreciated in our industry. Many professionals either overlook its potential, question its quality, or are uncertain about its legal implications. Yet, this technology has led to remarkable success stories. A standout example comes from the time when Richard and I worked together at Seiscan Geodata Ltd. and we were contracted by Cairn Energy to vectorise a significant amount of legacy seismic data from an Indian concession previously abandoned. The results were nothing short of spectacular—leading to major discoveries that benefited both Cairn and India. This case exemplifies the incredible value of revisiting and reinterpreting old data. Scepticism about reconstructed seismic often stems from past experiences with subpar implementations, particularly when a primitive software application fails to capture the full dynamic range of the data. This occurs with algorithms designed to only detect the positive-going, infilled peaks, ignoring the wiggle part of the trace. Ensuring your contractor uses full-waveform tracking technology is crucial for obtaining high-quality, reliable results. On the legal front, the counter-intuitive status of vectorised data might surprise some. Once vectorised, the SEG-Y output is legally yours, as it is considered a new creation distinct from the original data. In my experience, there has never been a serious or successful legal challenge to this. The series of numbers that constitute the output file resemble those of the original plot file only by statistical co-incidence. When plotted it may look precisely the same as the scanned section image, but legally it is something completely different. Vectorised data offers a cost-effective solution with vast potential for reconnaissance, planning new surveys, and even guiding decisions on purchasing new multi-client data. As Richard aptly notes, there's immense value in maximizing what we already have. Let's continue to explore and harness this powerful technology.

The concepts of Spherical Divergence and Gain Recovery in seismic data processing

Mind Map of Seismic Data,Acquisition ,QC,processing and interpretation

Understanding the existing seismic data used in your seismic survey design analysis is critical to the success of your seismic project. The basic questions that need to be answered include: 1. What existing data do you have? 2. What is the vintage of the existing data? 3. What are the parameters of the existing data? Answering these basic questions will prepare you for a successful seismic survey design.

Peter I. Pecholcs makes a good point that there are many instances in seismic acquisition and processing where a cool method works really well in one or a few cases but then not in many other cases. If you make judgement on the one or few cases, you may have deceived yourself. This is why I like working for a processing company where we see much data. Land seismic data varies a great amount in many ways. And there is much to be learned when a process works well in a few cases. The challenge is finding methods and concepts that work more reliably for different data in many cases.

Even with limited information about the subsurface, there are cases where modelling can play an important role in in the seismic workflow. Here is a link to a case study showcasing ray based modelling with minimum subsurface data. https://lnkd.in/dkR-cSTD

Another interesting land seismic data example! Note how the wavelet from a 50-feet depth source is very different from that at 100 feet and 5 feet. This shows how the thin near surface has a big impact on the data. A vibrator will have a bigger effect from the near surface. Understanding this helps acquisition and processing. More information is in my free SEG Distinguished Lecture, May 23: https://lnkd.in/gZU5zkXz

I recently spoke with Paul Fuellbrandt, the founder of Statvis Analytics Inc., an innovative software company based in Edmonton. I was truly inspired by his passion for developing cutting-edge tools to combat environmental contamination through data. One of Paul’s core values is to make data exploration easy and accessible, a philosophy I wholeheartedly share. I'm excited about the impressive features that StatVis implements using PAH data! For those unfamiliar, PAH data encompasses information related to polycyclic aromatic hydrocarbons—organic compounds that form during the incomplete combustion of organic materials like fossil fuels, wood, and tobacco. Understanding PAH data is crucial for identifying sources and effects in our environment, guiding pollution control efforts, and ultimately protecting public health. 🌍 Take a look at what StatVis is doing to redefine how we tackle environmental challenges! 🌱