Civitas Resources Weighs Sale of DJ Basin Assets

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Civitas Resources Weighs Sale of DJ Basin Assets

Civitas Resources Inc. is exploring a sale of part or all of its assets in the Denver-Julesburg Basin, which could be valued at more than $4 billion, people with knowledge of the matter said. The shale oil producer is working with a financial adviser to gauge buyer interest in the assets, according to the people, […]

Civitas Resources Inc. is exploring a sale of part or all of its assets in the Denver-Julesburg Basin, which could be valued at more than $4 billion, people with knowledge of the matter said.

The shale oil producer is working with a financial adviser to gauge buyer interest in the assets, according to the people, who asked not to be identified discussing confidential information.

Civitas would be open to divesting fully from the basin in Colorado if it receives a sufficiently attractive offer, the people said. The DJ Basin assets produce roughly 160,000 barrels of oil equivalent per day.

Shares in Civitas were up about 0.9% at 9:43 a.m. in New York, giving the company a market value of $5.2 billion.

Deliberations are ongoing and Civitas could decide not to proceed with a transaction, the people said. A representative for Denver-based Civitas declined to comment.

A sale of some or all of its assets in DJ Basin would free up cash that Civitas could use for acquisitions and to help pare its debt, which, according to data compiled by Bloomberg, stands at about $4.8 billion. Chevron Corp. and Occidental Petroleum Corp. are among the producers in the DJ Basin, which is harder to operate than most other US basins because of Colorado’s regulatory requirements.

Civitas also controls a sizable business in the Permian Basin, the most productive and profitable oil basin in the US. That’s thanks in large part to its $4.7 billion acquisition of assets from companies controlled by NGP Energy Capital Management in 2022.

Midsize shale producers have stepped up efforts to reshape their portfolios as they fight for survival in a fast consolidating industry. Ovintiv Inc. signed a pair of deals in November to exit the Uinta Basin and bolster its activities in Canada. Also that month, Coterra Energy Inc. bulked-up in the Permian by agreeing to acquire two companies for about $4 billion.

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What is Edge AI? When the cloud isn’t close enough

Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device. What technologies make edge AI possible? The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality. Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs. Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations. Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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