Technical Topics

Scaling buildup inside wellbores is a serious production problem that dramatically reduces the well productivity index. This issue has a significant cost across the industry, mostly associated with loss of production or additional operations such as well intervention.

The main goal of production logging is to evaluate the well or reservoir performance. A look at several scenarios of poor well performance and recommendations for production logging tools for diagnosis.

The Delaware Basin presented a challenging scenario for Anadarko due to the company’s tankless development approach. Sand filtration was identified as a perfect fit for this application.

Natural gas hydrates are considered to store vast amount of natural gas trapped in the subsurface worldwide. However, gas hydrates are yet to be commercialized due to technical challenges in the production technology.

When people hear of surfactant research in the oil and gas industry, they most likely think of surfactant flooding. An emerging area of surfactant application in the industry is as a conformance improvement agent.

This article gives a succinct overview of artificial intelligence, its emerging opportunities, prospects, and challenges, and concludes with recommendations to accelerate the admission of AI into workflows.

The term digital oil field has become a buzzword in the oil and gas industry these days, with the mention of it bringing up pictures of computers, flashy screens, and programming to mind. In reality, the concept goes beyond these.

Mitigation of stress shadowing could significantly enhance the impact of hydraulic fracturing treatments, increasing stimulated reservoir volume and leading to higher production of hydrocarbons.

Unconventional oil and gas have come to dominate the exploration and development scene in Western Canada since 2005, much as they have in the US. As usually happens with new play ideas and trends, appraisal and development have generated a broad spectrum of results in Canadian Unconventional plays.

Data story consumers are focused on summarized results and highlights instead of details of the analysis. It’s a data scientist’s responsibility to identify the significance of the data and to present it in a simple but scientific manner.