Seismic waves interacting with rock formations

Unlock Earth's Secrets: How Spectrum Decomposition Revolutionizes Oil Exploration

Lena Voss in Tech & Innovation August 2025 • 4 min read.

"Dive into the world of seismic data and discover how a cutting-edge technique is helping energy companies find oil hidden beneath the surface, even in complex geological settings."

The quest for oil drives innovation, particularly in exploration techniques. In the B block, flanking the Doba basin in Chad, Africa, the M prospect presented a complex challenge. Traditional methods struggled to accurately map the thickness of reservoir sands, vital for estimating oil reserves. To solve this, energy companies are turning to advanced methods like spectrum decomposition.

Between 2015 and 2016, the D-1, M-1, and M-2 wells were drilled, with M-1 and M-2 successfully striking oil. However, determining the extent and thickness of the reservoir remained challenging. This is where spectrum decomposition came into play, offering a way to interpret the seismic data and understand the reservoir's structure.

The goal was to semi-quantitatively determine the spatial sand thickness distribution of the tested reservoir. By applying spectrum decomposition analysis, the team aimed to resolve uncertainties in traditional seismic interpretation.

Spectrum Decomposition: A New Way to See Underground

Seismic waves interacting with rock formations

Spectrum decomposition works by breaking down seismic signals into their frequency components. It's like analyzing the different colors that make up white light to reveal hidden details. This technique can highlight subtle variations in the subsurface that might be missed by conventional seismic interpretation.

To understand how this works, consider the challenge: The reservoir sands in the M prospect are interbedded with shale, creating a complex geological environment. Traditional seismic interpretation, which often relies on a "wedge model" (where sand thickness is directly related to amplitude), can be misleading in these settings. A dim amplitude, which is when the signal from the oil is weak, can either indicate thin sand or interbedded layers.

Here’s how spectrum decomposition helps:

It identifies frequency responses associated with different sand thicknesses.
It helps differentiate between thin, interbedded sands and areas of zero sand thickness.
It improves the accuracy of reservoir models.
It ultimately leads to better estimates of oil reserves.

The team built four 2D geological models representing different sand/shale combinations. They used the P-velocity and density logs from the discovery wells to generate rock properties for each model. These models ranged from a single blocky sand to four thin sands interbedded with shale. By analyzing the seismic response of these models, the researchers developed a better understanding of how spectrum decomposition could be used to interpret the actual seismic data from the M prospect.

The Impact on Oil Exploration

The results from the spectrum decomposition modeling aligned with the drilling results. This confirmation reinforces the reliability of the technique. By integrating spectrum decomposition into their workflow, energy companies can improve their chances of success, reduce exploration costs, and optimize resource extraction. This approach not only helps in identifying potential oil reserves but also in understanding the geological complexities of subsurface environments.

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Everything You Need To Know

What does spectrum decomposition do in the context of oil exploration?

Spectrum decomposition is a method used in oil exploration that breaks down seismic signals into their different frequency components. This allows for a more detailed analysis of subsurface structures, helping to identify subtle variations that traditional seismic interpretation might miss. By analyzing these frequency components, spectrum decomposition can differentiate between various geological features, like thin layers of sand interbedded with shale, providing a clearer picture of potential oil reservoirs. This helps in estimating the thickness and extent of reservoir sands, which is crucial for determining oil reserves.

How was spectrum decomposition used to address challenges in the M prospect in Chad, Africa?

In the M prospect within the B block, spectrum decomposition helped overcome the limitations of traditional seismic interpretation, which struggled to accurately map the thickness of reservoir sands. The traditional method, relying on the "wedge model", could be misleading due to the complex geological environment where sands are interbedded with shale. Spectrum decomposition was applied to semi-quantitatively determine the spatial sand thickness distribution of the tested reservoir by analyzing the seismic data and understand the reservoir's structure. This ultimately helped in better estimates of oil reserves.

How does spectrum decomposition help in differentiating between thin sands and areas with zero sand thickness?

Spectrum decomposition identifies frequency responses associated with different sand thicknesses, which is crucial in distinguishing between thin, interbedded sands and areas with little to no sand. In the M prospect, this differentiation was vital because a weak seismic signal could indicate either thin sand layers or interbedded layers of sand and shale. Traditional methods may struggle with this ambiguity, but spectrum decomposition provides a more accurate assessment of the reservoir's structure, enhancing the reliability of reservoir models and leading to more precise estimates of oil reserves.

How was the reliability of spectrum decomposition confirmed, and what are its potential benefits for energy companies?

The reliability of spectrum decomposition was confirmed by comparing its results with the drilling results from the D-1, M-1, and M-2 wells. The alignment between the spectrum decomposition modeling and the actual drilling outcomes reinforced the validity of this technique. Integrating spectrum decomposition into exploration workflows can improve the success rate in identifying potential oil reserves, reduce exploration costs by providing more targeted insights, and optimize resource extraction by offering a more detailed understanding of subsurface geological complexities.

What role did 2D geological models play in understanding and applying spectrum decomposition?

The development of 2D geological models, ranging from single blocky sand to multiple thin sands interbedded with shale, played a significant role in understanding how spectrum decomposition could be applied to interpret seismic data. These models were based on P-velocity and density logs from discovery wells, allowing researchers to simulate various sand/shale combinations. By analyzing the seismic response of these models, the team gained insights into how spectrum decomposition could differentiate between different geological scenarios, ultimately leading to a more accurate interpretation of the actual seismic data from the M prospect. This modeling approach highlights the importance of integrating geological understanding with advanced seismic techniques for effective oil exploration.