Abstract

In unconventional oil and gas operations, evaluating well performance against type curves has become a common practice across various roles from Geologist to Engineer and required for Shareholder presentation materials. This involves individual efforts to extract and analyze data, leading to timeconsuming processes and potential errors. Our presentation proposes a technical solution by centralizing type curve data and well inventories. Through daily data extraction, we demonstrate the creation of a consolidated dashboard with performance metrics, enabling the real-time identification of variances to the type curve; this includes metrics such as EUR and IP90, providing stakeholders with a self-serve platform for consistent analysis. By minimizing ad-hoc analyses, this approach directs attention towards understanding production outcomes and inform future development decisions.

Introduction

In unconventional oil and gas operations, the assessment of well performance against type curves is an important part of validating performance, re-calibrating future type curves and informing development decisions. As mentioned by Freeborn and Russell in SPE 175967, the literature offers some confusion between the terms "type well" and "type curve". In this paper, we use the term type curve to refer to the rate-time production profile, most commonly created by averaging rate-time production profiles from analogous wells. This practice of comparison is most common in the Engineer's domain as they spend hours authoring type curves in technical tools, performing production re-forecasts in reserves systems, and looking at daily production actuals in the field data capture systems. However, other groups across the organization could also benefit from the same information such as Geologists and Land to Completions teams and Executives, but they are often less familiar with and further from the data. Multiple systems, data access, integrations, data resolution and sheer data size can make this a complicated and time-consuming task. This can then lead individuals with programming or data analytics skills down a path to create one-off tools for their own or for their team's use, which can lead to inefficiencies across the rest of the organization and significant time spent reconciling and correcting data across these bespoke systems. This paper proposes a technical solution aimed at centralizing type curve data such that it can easily be integrated with reserves forecasts and production actuals to streamline the evaluation and visibility of this information to all groups involved. As stated in SPE 158867, by Freeborn, Russell, and Keinick many operators report forecasting daily data over the first three to six months of the well's history. This paper will showcase examples based on the results of 62 producing wells with over 1000 days of daily production history, forecasts for each well and 4 type curves. Working with daily data makes the visuals more intuitive for the viewer and help avoid the distortions of aligning monthly data with daily operations. Additionally, having standardized metrics like Estimated Ultimate Recovery (EUR) for long-term and Average Production rate over 180 days (IP180) for shorter-term performance helps with objective comparisons, mitigating the challenges of subjective assessments based solely on visualizations. This standardized approach facilitates benchmarking performance across the organization, fosters a shared understanding of well performance metrics among diverse stakeholders and offers a selfserve platform comprised of consistent data for consistent analysis. By minimizing time spent building and troubleshooting ad-hoc analysis, this approach directs focus towards a more thorough understanding of production outcomes, which in turn, informs future type curve modeling and asset decision-making.

This content is only available via PDF.
You can access this article if you purchase or spend a download.