• Welcome to Saif Energy Limited : (051) 5913012

Author Archives: saifenergy

HomeSaif Energy Limited  →  Articles by: saifenergy
Rethinking Timing in Petroleum System Analysis: The Role of Migration LagRethinking Timing in Petroleum System Analysis: The Role of Migration Lag
Categories Blog, Drilling, exploration

Rethinking Timing in Petroleum System Analysis: The Role of Migration Lag

Author saifenergy

Rethinking Timing in Petroleum System Analysis: The Role of Migration Lag

Understanding petroleum systems is critical for oil and gas professionals. A common industry assumption holds that the timing of reservoir deposition relative to source rock maturity is crucial. This view suggests reservoirs deposited after oil generation might be barren or gas-prone.

Recent research by Zhiyong He (“Migration Lag – What is it and how it affects Charge Risk and Fluid Properties”) challenges this assumption. He demonstrates that the time between hydrocarbon generation and initial migration (“migration lag”) depends on generation rate and the volume needed to fill the source rock. This lag can represent 10-20% of the hydrocarbon generation window, or even longer for source rocks with reservoir-like properties.

The accompanying diagram (adapted from a Zhiyong He presentation) depicts a typical deep-water Gulf of Mexico burial history, highlighting source rock maturity. The Tithonian source rock entered the oil window 10-20 million years ago. However, the primary reservoirs in this region are middle Miocene, with some as young as Pleistocene. Notably, only in the last 5 million years have Miocene and Pliocene reservoirs begun filling with low-maturity oil. This occurs while the source rock generates gas, flushing out the oil in the initial carrier bed.

Rethinking Timing in Petroleum System Analysis: The Role of Migration LagRethinking Timing in Petroleum System Analysis: The Role of Migration Lag

Furthermore, He cites the Bohai region as another example. Here, the source rock is currently within the gas window, having entered it 10-20 million years ago. Despite this, the main reservoirs in the region hold mostly low-maturity oil.

Rethinking Timing in Petroleum System Analysis: The Role of Migration Lag

Conclusion: These findings suggest that timing may not be as critical a factor in exploration risk assessment as previously believed.

Reference
Migration Lag – What is it and how it affects Charge Risk and Fluid Properties*
Zhiyong He1
Search and Discovery Article #42014 (2017)
https://cva-academy.com/petroleum-systems-analysis-and-modeling.html

World's Largest Metamorphic Rock Oilfield Discovered
Categories Blog, exploration

World’s Largest Metamorphic Rock Oilfield Discovered

Author saifenergy

China Strikes Black Gold in Unexpected Place: World's Largest Metamorphic Rock Oilfield Discovered

In a groundbreaking announcement, the China National Offshore Oil Corporation (CNOOC) revealed the discovery of the world’s largest oilfield in metamorphic rock, situated in the Bohai Sea off the coast of eastern China. The Bohai 26-6 oilfield is located in the southern part of the Bohai Sea (Figure-1), at a relatively shallow depth of about 22 meters. The discovery of an additional 40 MMcmg (1.4 Tcfg) now brings Bohai’s proven reserves to 200 MMcmg (7.1 Tcfg), establishing it as reputedly the largest metamorphic rock oil field in the world (World oil).

Figure 1 Location Map of Bohaj Sea with major features after Hou et al. (2019)

This discovery challenges conventional notions, as the majority of oil and gas reserves are typically found in sedimentary rock formations, unlike the metamorphic rock hosting the Bohai 26-6 reservoir. Metamorphic rock undergoes intense heat and pressure, fundamentally transforming its original form. Traditionally, these rocks were not considered viable candidates for oil and gas exploration. However, the presence of hydrocarbons in this metamorphic reservoir signifies a major breakthrough in independent oil and gas exploration technologies.

Porosity preservation and difference from conventional metamorphic rocks

Due to the scarcity of technical data on the Bohai 26-6 field, attention has shifted towards studying the surrounding area to understand its structural styles, petroleum system, and reservoir characterization. A thorough research paper on the BZ 19-6 field is accessible online, providing detailed information used in this article. Proper citation of the author has been ensured wherever necessary.

The logs in the figure-2 show that there is a separation in the N-D logs against the Archean metamorphic granite rock.  According to Hou et al. (2019), The Archean metamorphic rock is dominated by metamorphic monzonitic granite, metamorphic diorite granite, gneiss and cataclasite intercalated with the later-stage intrusive bodies, such as dioritic porphyrite, ivernite and diabase. 

Figure 2 Reservoir Characterization of drilled section after Hou et al. (2019)

The typical log response in the igneous and metamorphic rocks according to Kansas Geological Survey is as under,

The neutron log

Open pores typically have very low volumes in igneous and metamorphic rocks.

  • low neutron porosity values in acid igneous rocks
  • fairly low neutron porosity values in basic igneous rocks, except for sub aerially weathered basaltic lavas
  • Low values in silica-rich metamorphics but increased values in micaceous rocks and very high values in chlorite schists.

The density log

The bulk density is a valuable diagnostic of igneous and metamorphic rock type.

  • acid igneous rocks have a lower bulk density
  • basic igneous rocks have a much higher bulk density
  • Siliceous metamorphic rocks generally have a lower bulk density than micaceous metamorphic rocks.

In the Archean metamorphic rock, the density is high, and the neutron count, a direct indicator of hydrogen atoms, also increases. However, in the Kongdian Formation, both logs almost overlay. The elevated neutron value suggests additional hydrogen in the logged interval that is provided by either hydrocarbons or sufficient clay content. Additionally, there is notable porosity (5-6%) in the cores, with permeability below 2 milli darcy, as depicted in the figure above. Let’s assess if this aligns with the core details and petrology.

Sidewall cores, thin sections, and SEM analysis reveal well-developed karstification and weathering dissolution zones within the metamorphic rocks. Furthermore, Hou et al. (2019), asserts that there’s evidence suggesting the possibility of cryptoexplosion of supercritical fluid within the Archean buried-hill metamorphic granite. This event could have led to the formation of cryptoexplosive breccia or cryptoexplosive tuff, promoting thermal fluid filtrated alteration. Consequently, high-permeability reservoirs may have formed, containing abundant pores and fractures.

The Bohai 26-6 oilfield is situated primarily within a buried hill (geological feature), its reservoir part comprises of fractured and weathered basement rocks. Additionally, an overlying layer of coarse-grained clastics hints at the potential for additional oil reserves in this stratum. This scenario mirrors similar observations made by Henk Kombrink, GEO ExPro, such as the Lancaster field in the West of Shetlands, UK waters. In such cases, production extends beyond fractured basement rocks to include overlying coarse-grained clastic rocks. It’s intriguing to explore whether this phenomenon repeats itself in China, especially given the thicker upper section.

This find not only holds significant economic potential for China but also represents a crucial step forward in the diversification of global energy sources. As the world continues to navigate the complexities of the energy landscape, the Bohai 26-6 discovery serves as a reminder of the potential for innovation and the ongoing quest for sustainable energy solutions.

Key Points:
  • CNOOC discovered a large oilfield in the Bohai Sea, claimed to be the world’s largest in metamorphic rock.
  • The oil is found in a buried hill, where the reservoir is primarily fractured and weathered basement rock.
  • The presence of overlying coarse-grained clastics raises the possibility of additional oil reserves in this layer.
References
  • Characteristic and controlling factors of deep buried-hill reservoir in the BZ 19-6 structural belt, Bohai sea area:
    Mingcai Hou a b, Haiyang Cao a b, Huiyong Li c, Anqing Chen a b, Ajuan Wei c, Yang Chen a b, Yuechuan Wang c, Xuewei Zhou a b, Tao Ye c
Categories Blog, Drilling

Leveraging Artificial Intelligence for Enhanced Log Analysis in the Oil and Gas Industry

Author saifenergy

In recent years, Artificial Intelligence (AI) has emerged as a transformative force across various industries, and the oil and gas sector is no exception. One area where AI is making significant strides is in log analysis—a crucial aspect of petrophysics that plays a vital role in identifying hydrocarbon reservoirs and evaluating their properties. In this blog post, we’ll explore how AI is revolutionizing log analysis in the oil and gas industry, and the key techniques and technologies driving this transformation.

The Impact of AI on Log Analysis:

Traditionally, log analysis has been a time-consuming and complex process, requiring a high level of expertise. However, AI is changing the game by automating many of the steps involved in log analysis. Machine learning algorithms, such as neural networks, are being trained to recognize patterns and anomalies in log data, enabling companies to identify potential hydrocarbon reservoirs more quickly and accurately than ever before.

Image processing is another powerful technique that AI is bringing to the table. Logs can be conceptualized as 1D images, with each curve representing a different rock property. By applying image processing techniques, such as convolutional neural networks, AI algorithms can identify features in the log curves that are indicative of hydrocarbons, further enhancing the accuracy of reservoir identification.

Python: The Key to Visualization and Interpretation:

Python, a versatile and powerful programming language, is playing a crucial role in visualizing and interpreting log data. With its robust capabilities in data analysis and visualization, Python is being used to create custom log analysis workflows tailored to each company’s specific requirements. This flexibility is particularly valuable in complex reservoir environments, where off-the-shelf solutions may fall short. Python’s simplicity and efficiency make it an ideal tool for optimizing exploration and production strategies.

The Future of AI in Log Analysis:

As AI technology continues to evolve, we can expect even more exciting developments in the field of log analysis. Advancements in machine learning algorithms, coupled with improvements in image processing techniques, will further enhance the speed and accuracy of reservoir identification. Additionally, the integration of AI with other emerging technologies, such as big data analytics and cloud computing, holds the potential to unlock new insights and opportunities for the oil and gas industry.

Conclusion:

In conclusion, AI is revolutionizing log analysis in the oil and gas industry, enabling companies to extract more insights and value from their data than ever before. By leveraging machine learning algorithms, image processing techniques, and the power of Python, companies can streamline the process of identifying hydrocarbon reservoirs and optimize their exploration and production strategies. As AI technology continues to advance, the future looks bright for the oil and gas industry, with AI-powered log analysis leading the way towards greater efficiency and success.

Navigating the Shale vs. Clay Dogma in Petrophysical Exploration
Categories Blog, Drilling, exploration

Navigating the Shale vs. Clay Dogma in Substitute Exploration With Evalation

Author saifenergy

In the realm of substitute exploration with evalation, the interchangeable use of the terms “shale” and “clay” has been a historical norm. However, as the industry acknowledged that shale can harbor hydrocarbons, a profound shift occurred. My journey into the literature on this subject felt akin to navigating a complex quagmire!

The standard approach to calculating Vshale and Vclay suggests that understanding the nuances of what you’re calculating is crucial for estimating accurate volumes. While this concept holds well in pure shale intervals, the waters become murkier in shaly sand intervals, casting doubt on calculations.

A pivotal question emerges: What if shale is present in sandstone reservoirs? Existing literature reports three types of shale distribution: laminated shale, structural shale, and dispersed shale. Notably, none of these distributions categorize shale as clay.

I propose that laminar and structural shales are indeed shales, while dispersed shales are, in fact, clays. Intrigued by the assertion? Let’s delve into it.

Dispersed clays can form either through feldspar alteration (authigenic clays) or settle with sand sediments, forming a thin coating that may subsequently clog the pore network. Rip-up clasts, a type of structural shale, can be deposited in sandstone, appearing as part of the weight-bearing grains. On the other hand, authigenic clays, formed from feldspar alteration, can transform into kaolinite, occurring as clumps that may choke pore space.

In muddy environments, clays can settle with sand grains, forming coatings such as smectite and chlorite on sand grains. Smectite can alter to illite due to temperature and pressure, replacing Na cations with K to form illite. This suggests that the dispersed shale mentioned in the literature is, in fact, clay.

While geological concepts persistently differentiate between shale and clay, the coexistence of these entities remains elusive and warrants attention for a better understanding of complex shaly sand reservoirs.

In conclusion, let’s avoid confusion between shaly sands and sandstone with clays. Embracing a nuanced perspective can pave the way for a more accurate comprehension of the intricacies within petrophysical exploration.

Navigating the Shale vs. Clay Dogma in Petrophysical Exploration
1 2 3 4 5 … 8