In a major development for Pakistan’s energy sector, Attock Refinery Limited (ARL) has temporarily shut down its main crude distillation unit. This unit processes 32,400 barrels of crude oil per day. The shutdown happened due to extremely low crude stock levels after several local gas fields in Khyber Pakhtunkhwa (KPK) were closed.

The ARL management confirmed the decision in an announcement to the Pakistan Stock Exchange on May 27, 2025. The refinery’s main unit will stay offline until June 1, 2025. This move comes as the country struggles to balance local gas production with an increased inflow of imported liquefied natural gas (LNG).

Why Did the Shutdown Happen?

Adil Khattak, Managing Director of ARL, explained the reason behind this decision. The government temporarily closed several gas fields to manage high pressure in the country’s main gas pipeline. This pressure spike resulted from excess LNG imports, raising line pack pressure beyond 5 billion cubic feet (bcf).

To control this, authorities restricted outflows from local gas fields. This action immediately reduced both gas and crude oil production. With limited crude oil available, ARL had no option but to halt operations at its key unit. The situation is already affecting the petroleum and refining sectors. Many fear this could disrupt the energy supply chain for industries and consumers.

Impact on the Energy Industry

This incident highlights ongoing weaknesses in Pakistan’s energy infrastructure. Heavy reliance on imported LNG, combined with limited domestic gas storage, has created supply bottlenecks. The disruption at ARL shows how fragile the country’s refining operations remain under these conditions.

Energy experts urge the government and industry leaders to prioritize infrastructure upgrades. They recommend expanding gas storage, improving pipeline systems, and developing an integrated supply chain to prevent future crises.

In the era of big data, geoscience stands at the crossroads of tradition and transformation. With petabytes of seismic, well log, and core data available, Artificial Intelligence (AI) offers a way to cut through the complexity of hidden geological past to uncover faster, delivering tangible results/findings by reducing human bias, to reach smarter exploration decisions.

In an industry built purely on data, yet driven by interpretation, geoscience has always been a fertile ground for innovation. From seismic waves echoing through the subsurface to the tiniest porosity variation in a core sample, geoscientists are constantly deciphering nature’s complex code. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools to assist in this journey offering unparalleled speed, scale, and groundbreaking statistical strength which human mind could never accomplish.

But is AI the game-changer, we think it is? Or are we walking a tightrope between automation and abstraction?

Why AI, and Why Now?

The oil and gas industry like many others is grappling with the twin pressures of cost efficiency and smarter decision-making. Exploration and development decisions hinge on interpreting massive datasets: seismic volumes, well logs, core data, production history, studies report spanning 1000s of pages and more. AI provides a way to mine these datasets for patterns, predictions, and details that might otherwise go unnoticed or take months of human effort.

Where AI is Making an Impact in Geoscience

AI is no longer a theoretical tool. Here’s where it’s actively being deployed:

• Seismic Interpretation
  Deep learning models are assisting in fault and horizon detection, seismic facies classification, and amplitude anomaly recognition. What once took weeks of manual effort is now achievable in hours with the right training data and labels.
• Petrophysical Analysis
  Algorithms are automating log interpretation, identifying lithofacies, and even predicting mineral volumes in unconventional plays,  and other rocks properties such as porosity, permeability, Sw etc. Random Forests, XGBoost, SVM, PCA and Neural Networks are now part of the modern petrophysicist’s toolbox.
• Reservoir Modeling
  AI helps streamline history matching and can optimize property modeling by learning from dynamic behavior, especially in complex carbonate and fractured reservoirs.
• Thermal and Basin Modeling
  From burial history to source rock maturity, AI models can simulate and predict based on sparse calibration data reducing dependency on deterministic workflows.
• Matching Core Data with Log Data AI is bridging the gap between high-resolution core analysis in labs, core photos, core reports and the downhole log data, making it easier to integrate them all.
• Pressure Predictions and Geomechanics Trained ML models on old well data, are predicting pore pressure trends at lightning speed, which helps in everything from drilling safety to designing wells.

ML First, DL Later?

While deep learning catches most headlines in AI, yet the majority of geoscientific applications today rely on classical machine learning—Random Forests, XGBoost, and clustering techniques. These models are used for their interpretability and ability to work with structured data like well logs and petrophysical curves.

Deep learning, on the other hand, is making strides primarily in seismic interpretation and image-like data domains. But due to its hunger for labeled data and computational power, its adoption is still more limited compared to traditional ML at least for now until we have trained models on every kind of data. To unlock its full potential, we need comprehensive training datasets that includes all relevant labeled data, horizons, faults, well logs, VSPs, and more. Seismic imagery, in particular, plays a critical role in advancing these applications, enabling more meaningful interpretations and paving the way for future breakthroughs

Open-Source AI in Geoscience: Real Models, Real Impact

One of the most exciting developments in recent years has been the open-source release of AI models trained specifically on geoscientific data. These models have emerged from both industry-led competitions and collaborative research projects and these datasets and models are putting cutting-edge technology into the hands of everyone, sparking innovation and making advanced tools accessible to all. These open-source datasets are more than just tools—they’re opportunities. Anyone can use them to deploy their own AI models, contributing new ideas and advancing the field. By sharing solutions openly on platforms like GitHub, researchers and developers are creating a community where innovation thrives and progress is accessible to all.

1. FORCE 2020 Machine Learning Competition (Norway)

One of the landmark events was the FORCE ML competition, organized by the Norwegian Petroleum Directorate in 2020. The goal was to build machine learning models for lithology prediction and fault identification using open North Sea well data.

• Tasks: Lithofacies classification from well logs and fault interpretation from seismic.
• Data: Released public well logs and seismic volumes from the Norwegian Continental Shelf.
• Results: Dozens of teams submitted models using Random Forests, Convolutional Neural Networks (CNNs), and ensemble methods.
• Outcome: Many of the winning models and notebooks were made open-source on GitHub, enabling others to build upon them.

Example Repo: https://github.com/bolgebrygg/force2020
Datasets: https://www.force2020.net

2. The SEG Machine Learning Contests (2019–2022)

The Society of Exploration Geophysicists (SEG) launched several AI competitions, particularly around salt body segmentation in seismic data. These contests offered open access to labeled seismic datasets, pushing forward the use of deep learning in geoscience.

• Popular Models Used: U-Net architectures for segmentation, often implemented in TensorFlow or PyTorch.
• Learning Outcome: Seismic image segmentation became a benchmark task for geoscientific AI.
• Key Benefit: It created reusable architectures for other segmentation tasks like faults, channels, and facies.

Example Repo: https://github.com/seg/2020-ml-contest

3. OSDU (Open Subsurface Data Universe)

OSDU is creating a massive shared data platform for the industry with the intention of enabling more AI research. Several AI-ready datasets and connectors are being published to make reproducibility and deployment easier across companies.

OSDU Community: https://osduforum.org

What Are the Advantages of AI?

Speed and Efficiency
Models can scan and interpret vast datasets in a fraction of the time a human would need.

Pattern Recognition Beyond Human Perception
AI detects subtle relationships in data like seismic signatures correlated to lithology changes that may elude traditional methods.

Consistency and Objectivity
Unlike humans, AI does not tire or introduce interpretation bias assuming the model is well-trained.

Data Fusion
ML models can combine logs, seismic, production, and even textual reports to generate holistic insights.

Scalability
Once trained, models can be applied across multiple fields or basins with minimal tweaks.

And the Limitations?

Despite the promise, these are some real-world limitations:

• Quality of Training Data
  The model is only as good as the data it learns from. Poor data quality or missing labels leads to unreliable outputs.
• Black Box Models
  Deep learning models can lack transparency. Geoscientists may struggle to understand why a model made a certain prediction.
• Overfitting and Generalization
  A model trained on one basin may not generalize to another without retraining or revalidation. If the testing data is different from the training data, the outcome may be authentic which needs care.
• Geological Context Still Matters
  AI may identify patterns, but it doesn’t understand tectonics, depositional systems, or the basin’s geologic history unless you teach it indirectly.
• Misplaced Trust in Automation
  Relying entirely on AI without domain knowledge can lead to oversights. AI should augment, not replace, geoscientific intuition.

A Glimpse into the Future

The best results seem to come from hybrid models—where physics-based understanding is combined with data-driven prediction. For instance, integrating Rock Physics templates with AI-driven facies classification leads to more robust models.

The future may also see AI helping with probabilistic interpretation, real-time drilling optimization, and even automated basin screening—all while being explainable and trustworthy.

The Future of AI in Geoscience

AI isn’t going to replace geoscientists anytime soon, what it’s really going to do is freeing them up to focus on more complex, high-level questions. As tools get better and datasets become more accessible, we’re likely to see advances in areas like:

• Hybrid models combining AI with physics-based approaches
• Explainable AI tools that make black-box models more transparent
• Open collaborations between researchers, startups, and companies

Conclusion

AI is a tool a very powerful one but it works best when paired with human expertise. The real winners in this space will be those who can blend advanced algorithms with a deep understanding of geology.

In a significant move towards advancing Pakistan’s mineral resources, Oil and Gas Development Company (OGDC) has approved an increase in its funding commitment to $627 million for the Reko Diq copper and gold mining project. This decision follows the completion of an updated feasibility study, reinforcing Pakistan’s efforts to unlock one of the world’s largest copper and gold reserves.

Reko Diq Project Overview

Located in Chagai, Balochistan, the Reko Diq project is a multibillion-dollar mining initiative with enormous potential. The updated feasibility study estimates a mine life of 37 years, divided into two operational phases. Phase-I, requiring a capital outlay of $5.6 billion (excluding financing costs and inflation), is set to commence operations in 2028. By 2034, Phase-II will double the processing capacity to 90 million tonnes annually.

OGDC’s Enhanced Commitment

OGDC’s board approved the $627 million investment, which includes the company’s share of project financing costs. This revised commitment reflects the anticipated increase in copper and gold prices, which will contribute to offsetting the project’s higher costs. The company’s proportional equity contribution is expected to be $349 million, subject to adjustments for actual financing costs and inflation.

Collaborative Stakeholders

OGDC holds an 8.33% stake in Reko Diq, as part of a collective 25% share held by three Pakistani state-owned enterprises, including Pakistan Petroleum Limited and Government Holdings (Private) Limited. The project’s primary operator, Barrick Gold Corporation, holds a 50% stake, while the Balochistan government has a 25% interest, divided into a 15% fully funded stake and a 10% free carried stake.

Financing and Growth Potential

A limited-recourse financing facility of up to $3 billion is being negotiated to support the project’s initial phase, supplemented by shareholder contributions. The feasibility study also highlights the potential for future growth, with the project targeting five of the 15 identified porphyry surface expressions under the current mining lease.

Economic and Social Impact

The Reko Diq project is expected to produce 13.1 million tonnes of copper and 17.9 million ounces of gold over its lifespan. It will contribute significantly to Pakistan’s economy through job creation, local community development, and increased revenue streams. Additionally, the project will pave the way for technological advancements and infrastructure improvements in Balochistan.

Conclusion

OGDC’s commitment to the Reko Diq project marks a pivotal moment in Pakistan’s energy and mining landscape. By leveraging its strategic investments, the company is set to play a crucial role in unlocking the full potential of Pakistan’s mineral wealth, driving economic growth, and enhancing national prosperity.

The oil and gas industry operates through three core sectors: Upstream Midstream Downstream Key Differences in Oil & Gas between these sectors is essential for industry professionals, investors, and stakeholders. Each stage plays a crucial role in transforming raw resources into refined products for end-users. Let’s break down how each sector functions and its significance in the energy supply chain.

Upstream: Exploration and Production

The upstream sector focuses on discovering and extracting crude oil and natural gas from underground reservoirs. It includes both onshore and offshore operations.

• Key Activities: Exploration, drilling, well completion, and production.
• Assets: Oil fields, drilling rigs, and production platforms.
• Challenges: Geological uncertainties, drilling risks, and price volatility.

Midstream: Transportation and Storage

Once the oil and gas are extracted, the midstream sector takes over. This stage involves transporting, storing, and marketing crude oil and natural gas.

• Key Activities: Pipeline transportation, storage, and wholesale distribution.
• Assets: Pipelines, storage tanks, and terminals.
• Challenges: Safe transportation, logistics management, and regulatory compliance.

Downstream: Refining and Distribution

The downstream sector handles refining crude oil into usable products like gasoline, diesel, and petrochemicals. It also includes product distribution and retail.

• Key Activities: Refining, manufacturing, and consumer distribution.
• Assets: Refineries, petrochemical plants, and retail networks.
• Challenges: Supply chain management, regulatory adherence, and market demand shifts.

Key Takeaways

• Focus: Upstream (exploration), Midstream (transportation), Downstream (refining).
• Products: Crude oil and gas (upstream), transported resources (midstream), refined products (downstream).
• Challenges: Upstream faces geological risks, midstream manages logistics, and downstream handles market demand.

Conclusion

Understanding the distinctions between upstream, midstream, and downstream highlights the complexity of the oil and gas industry. Each sector plays a vital role in transforming raw resources into essential products, ensuring a seamless journey from exploration to end-user delivery.

Upstream Midstream Downstream Key Differences in Oil & Gas