Unlocking well productivity drivers in Eagle Ford and Utica unconventional resources through data analytics

2019 ◽  
Vol 71 ◽  
pp. 102976 ◽  
Author(s):  
Clay Kurison ◽  
Huseyin Sadi Kuleli ◽  
Ahmed H. Mubarak
2021 ◽  
Author(s):  
Cyrus Ashayeri ◽  
Birendra Jha

Abstract Decision making in new fields with little data available relies heavily on physics-based simulation models. However, due to a lack of full understanding of the physical processes governing flow in the unconventional resources, data-driven modeling has emerged as an alternative and complimentary tool to create recovery forecasts that honor the available data. Transfer Learning provides an opportunity to start early-stage analysis of the asset before adequate data becomes available. New challenges in the energy industry as well as shifting dynamics in both domestic and global supply and demand has encouraged some of the petroleum exporting countries in the Middle East to strategize the development of unconventional resources. In this research we have developed a data-driven Transfer Learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed Transfer Learning method is developed on real-world data from several thousand horizontal multistage wells in the Eagle Ford super-basin in South Texas. In this method we have integrated reservoir engineering domain expertise in the data pre-processing and feature generation steps. We have also considered the temporal and spatial balancing of the training data to assure that the predictive models honor the real practice of unconventional field development. Our full cycle Transfer Learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. This workflow enables reservoir engineers to experiment with multiple hypothetical scenarios and observe the impact of additional data in the learning process. We use the developed workflow to examine the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East. Existence of all liquid types of oil, condensate and dry gas in the Eagle Ford has resulted in training a model flexible enough to be tested on various types of assets in a new location. We first present the successful deployment of our model within the Eagle Ford. Next, we use the information from major formations such as Tuwaiq Mountain and Hanifa and show the value of a pre-existing model from a fully-developed shale play on achieving acceptable accuracies with minimal information available in a new field. Our model is developed by data types with relatively low resolution that minimizes overfitting effects and allows generalization to different geologies with basin-wide accuracy. This approach allows conducting accelerated assessment of various sections of a large asset to enhance field development planning processes. This is a first example of such an effort on a basin scale that examines the effectiveness of Transfer Learning on some of the major unconventional plays in the Middle East region. This workflow allows investigating the relationship among geologic and petrophysical variables, drilling and completion parameters, and productivity of a large group of wells in a new asset.


2015 ◽  
Author(s):  
C.N.. N. Fredd ◽  
J.L.. L. Daniels ◽  
J.D.. D. Baihly

Abstract The industry has made significant advances in the way we exploit unconventional resources such as source rock and tight reservoirs. Innovations in horizontal drilling and multistage fracturing have unlocked previously uneconomical plays, and technology has brought a step change in operational efficiency. Lessons learned from unconventional resources highlight collaboration and integrated reservoir-centric workflows as common traits for economic success. The development of unconventional resources in North America was aided by the readily available infrastructure, water resources, expertise, and a general understanding of potential sweet spots due to numerous well penetrations. Even with these favorable conditions, an estimated 40% of unconventional wells are uneconomical due to spatial variability in reservoir characteristics, lateral heterogeneity along the wellbores, accuracy of well placement, and variability in drilling, completion, and stimulation practices. This non-ideal economic performance also ineffectively consumes local resources such as water and proppant. This paper provides a retrospective assessment of the Barnett Shale and Eagle Ford Shale to highlight lessons learned and the associated value of those learnings. The impact of applying technology and utilizing a data-driven approach based on measurements will be assessed in terms of the investment required to achieve a given hydrocarbon production. The results indicate that these unconventional plays could have been developed with well counts reduced by the thousands, water consumption reduced by billions of gallons, and investment savings in the billions of dollars if initially exploited by applying the key lessons learned from over the past 30 years. This potential reduction in investment amounts to $40 billion for the Barnett Shale (shale gas) plus the Eagle Ford Shale (oil window) and represents the significant value of moving along the learning curve. Fortunately, there is no need to repeat this learning curve investment, as key lessons learned can be applied to other unconventional plays around the world. This learning curve is of specific value in international plays where local infrastructure, supply, and market conditions may not be as favorable as in North America, hence necessitating a different approach to optimize the overall investment when developing unconventional plays.


Sign in / Sign up

Export Citation Format

Share Document