Bibliographic record
Abstract
Private equity and venture capital firms are just as important to the evolution of the oil and gas industry as the leading operators and service companies. This became evident during the oil price crash when public equity opportunities disappeared and lenders shied away from the industry amid scores of bankruptcies. Illustrating the point earlier this year was Wil VanLoh, founder and CEO of Quantum Energy Partners, who boasted during the NAPE Global Business Conference that his firm would “look really similar to a superindependent” if it were to aggregate all the companies in which it’s currently investing, which altogether tally 2.3 million net acres, 350,000 BOE/D, and 32 rigs working across the US and Canada. With the backing of private investment, entrepreneurs who previously worked for companies such as Shell, Anadarko, Halliburton, Baker Hughes, and even smaller, lesser known entities have applied their unique skills and ideas to find, develop, and produce the latest generation of prolific oil and gas fields quicker and with less money than ever before. But finding those innovators who are actually capable of turning their dreams into reality is difficult. And when you’re the firm tasked with bearing the financial risk that comes with backing fledgling companies that hope to break into an already volatile industry, you tend be choosy. Enter Charlie Leykum, founder of CSL Capital Management, a 10-year-old private equity firm that builds and acquires controlling interests in oil and gas service and equipment companies. It’s currently launching its $1.5-billion Fund III as well as a special-purpose acquisition company, which raises capital from public investors, enabling them to participate in private equity purchases. “So much of what we do is finding not just an individual or a group of individuals who are technically or commercially really savvy,” but finding a “harmonized, well-working team,” Leykum said during the SPE Gulf Coast Section’s recent Innovation Entrepreneurship Symposium. CSL only considers business plans where a team is either in place or assembling with “a subject matter expert who’s the key decision maker” as well as technical and operations personnel. Together, they must have a firm grasp of the challenges and risks involved in building a business. When looking at possible investments, CSL wants technical leadership in a market. When CSL invested in a fracturing company in the early 2000s that ended up successful, Leykum said, the firm partnered with a major service company’s “lead fracturing executive for the business line” whose skills and expertise could solve—and had solved—unique challenges in different basins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".