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Exploring the Offshore Indus Basin Opportuintites
Categories Blog, exploration

Exploring the Offshore Indus Basin: Opportunities and Challenges

Author saifenergy

Exploring The Offshore Indus Basin: Opportunities And Challenges

The Offshore Indus Pak G2-1, the deepest well drilled in the Offshore Indus in terms of water depth, offers interesting insights into the region’s hydrocarbon potential. Despite its depth, the 1D burial history graph (Figure 1) indicates that the well did not achieve the required burial to crack any hydrocarbons. This well, drilled near the Saurashtra Volcanic Arch, encountered a reef, yet remained dry, and the play couldn’t be established.

Reservoir Insights:

Drilling data reveals two sets of proven reservoirs in the Offshore Indus Basin

  • Widely Distributed Miocene Channel Sandstones
  • Locally Distributed Paleocene–Eocene Reef Limestone

Analogies with the adjacent Kutch Basin and the onshore Indus Basin suggest the possible existence of Cretaceous sandstone reservoirs in the Offshore Indus Basin.

Challenges and Historical Context:

The well stopped in the reef limestone after drilling over 200 meters, and unfortunately, it was dry. It was believed that the adjoining synclines would have generated hydrocarbons that would have migrated to the highs (the reefs) but the concept failed. Since the well was stopped early in the reef, remodeling of data in the context of basin is quite difficult. Had the well penetrated the basement, it would have been quite interesting. It was also believed that the reef would be riding all over the basement rocks.  The only proven reservoir is Middle Miocene deltaics that have produced gas in Pak Can-01, but the gas column was too small to justify infrastructure development.

It seems that volcanic activity plays a significant role in the evolution of the offshore part of the Indus Basin and its implications have far-reaching consequences on the hydrocarbon potential.

There have been two major volcanic events in the sea area of Pakistan:

Basalt Eruption of Somnath Ridge (~70 Ma)

Basalt Eruption of Deccan-Reunion (Reunion Mantle Plume, ~65 Ma)

According to Calvès et al. (2010), the basalt eruption of Somnath Ridge contributed to the formation of the volcanic basement in the southeastern Offshore Indus Basin, particularly around Somnath Ridge and Saurashtra High. This area covers approximately 45,000 km².

Geological Insights:

Seismic data interpretation indicates (Figure 2) (that in the southeastern part of the basin adjacent to Somnath Ridge and Saurashtra High, Deccan basalts are distributed in the marine-facies strata of the Upper Cretaceous–Paleocene in a laminated form (Khurram et al., 2019). The northwestern part, far from the Reunion mantle plume, has minimal basalt impact but is close to the strike-slip fault zone of Murray Ridge, making it a potential focus for future oil and gas exploration.

Geothermal Gradients:

Somnath Ridge: Low geothermal gradient of 33℃/km.

Sedimentary Center: High geothermal gradient of 37℃/km – 55℃/km, aiding source rock maturity (Calvès et al., 2010).

The northwestern part, with its developed faults near Murray Ridge, presents an interesting area for future exploration. There are chances that the established Cretaceous plays would be found there (Figure-02).

In most of the wells drilled, the source rocks are in oil window but Pak Can-01 produced gas suggesting that the gas would have been migrated from the deeper part of the basin. Modeling suggests that the Paleocene source rocks (effective in the adjoining onshore) may have become post mature at the end of Oligocene suggesting a charge to the Miocene and younger reservoirs by shallower source rocks.

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Exploring the Offshore Indus Basin

Figure 2 . After Shahzad et. al.

References:

Calves G, Schwab AM, Huuse M, Peter DC, Asif I.  2010.  Thermal regime of the northwest Indian rifted margin Comparison with Predictions. Marine and Petroleum Geology, 27, 1133–1147.  doi: 10.1016/j.marpetgeo.2010.02.010.

Chatterjee S, Goswami A, Scotese. 2013. The longest voyage: Tectonic, magmatic, and paleoclimatic evolution of the Indian plate during its northward flight from Gondwana to Asia. Gondwana Research, 23, 238–267. doi: 10.1016/j.gr.2012.07.001.

Jian-ming Gonga, b, c, Jing Liaoa, b, *, Jie Lianga, b, Bao-hua Leia, b, Jian-wen Chena, b, Muhammad Khalid, Syed Waseem Haidere, Ming Meng. Exploration prospects of oil and gas in the Northwestern part of the Offshore Indus Basin, Pakistan

Shahzad K, Betzler C, Ahmed N, Qayyum F, Spezzaferri S, Qadir A. 2018. Growth and demise of a Paleogene isolated carbonate platform of the Offshore Indus Basin, Pakistan: Effects of regional and local controlling factors. International Journal of Earth Sciences, 107, 481–504. doi: 10.1007/s00531-017-1504-7.

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.

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