Role of Multivariate Analysis to Define the Key Driver's Governing the Well Productivity in Marcellus Shale Play

Gupta, Mohit; Jain, Promel; Shrivastava, Akhil; Srivastava, Mukul; Deka, Bhabesh

doi:10.15530/urtec-2016-2460969

Cited by 3 publications

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“…No estudo de Hess 2016, a variável preditora, mesmo sendo numérica, assumindo valores 0 (zero) para eventos de não prisão e 1 (um) para eventos de prisão, pode ser considerada qualitativa pois estes números (0 e 1) não qualificam a variável, são apenas referências ao cálculo matemático. Por outro lado, para o modelo de previsão de produção, descrito por Gupta et al (2016), já está adequado o uso de regressão linear múltipla, pois o valor de produção de petróleo é um valor métrico e contínuo.…”

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Desenvolvimento de solução CRISP-DM para classificação do evento prisão de coluna no processo de perfuração de poços offshore

Pinto¹

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Over the years, the Exploration and Production (E&P) industry, in order to improve productivity and reduce costs, has sought the use of digital technologies to mitigate the effects of seasonal oil prices, the shortage of professionals, increased competition and to promote safer operations. Today the digital transformation initiativereferred to as the new industrial revolutionhas been driving the use of disruptive technologies and provoking cultural changes, both in society and industry. At the heart of this revolution is the exponential growth and data availability. In this scenario, a change is needed in the way these data are collected, stored, analyzed and accessed to support organizations intelligence and decision-making cycles. This dissertation addresses the process of knowledge discovery in data, through the development of a computational solution with support of the methodology CRISP-DM (Cross Industry Standard process for data Mining) for analysis of anomalies in the process of Drilling of wells in ultra-deep waters. Well drilling is one of the stages that most demand financial resources. Thus, understanding and anticipating problems, evaluating their causes and planning solutions are necessary for global cost control, in order to ensure the well integrity and stability, avoiding non-productive times. This research was delimited to the stuck pipe event and the use of the logistic regression method in the development of the modeling step. The data used were made available by the Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP), through a development and innovation research Project (PD&I), containing a set of real data for drilling oil wells of the Pre-salt polygon on the Brazilian coast. At the end of the evaluation process, it was possible to analyze the applicability of the chosen model and the results obtained with the business perspective, that is, that the results are adequate to support the decision-making of the organization. The results obtained to exemplify the model demonstrate an accuracy of 89% with a rate of 99%. Another important result of the work is the contribution to professionals and companies that need to apply methods of data science in similar cases or with other characteristics. Some limitations with low data quality and sample size are also highlighted during the knowledge discovery process. Additionally, programming language codes were included, used for understanding and processing data and generating results. These can serve as an initial version for other analyses.

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Section: Modelagemunclassified

Desenvolvimento de solução CRISP-DM para classificação do evento prisão de coluna no processo de perfuração de poços offshore

Pinto¹

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Stratigraphic characterization of the Eagle Ford shale to identify the best landing zone: A semianalytical workflow

2022

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Placement of the horizontal well within the best landing zone is critical to maximize well productivity, thus identification of the best landing zone is important. This paper illustrates an integrated semi-analytical workflow to carry out the stratigraphic characterization of the Eagle Ford shale to identify the best landing zone. The objective of this work is twofold: 1) to establish a workflow for stratigraphic characterization and 2) to understand the local level variability in the well performance.To establish the workflow, we have used the production data, petrophysical information and regional reservoir property maps. As a first step of the workflow, we subdivided the Eagle Ford shale into nine smaller stratigraphic units using the wireline signatures and outcrop study. In the second step, we have used statistical methods such as linear regression, fuzzy groups and theory of granularity to capture the relationship between the geological parameters and the well performances. In this step, we identified volume of clay (Vclay), hydrocarbon filled porosity (HCFP) and total organic carbon (TOC) as key drivers of the well performance. In the third step, we characterized the nine smaller units and identified four stratigraphic units as good reservoirs with two being the best due to their low Vclay, high HCFP and high TOC content.Finally, we reviewed the well paths of four horizontal wells with respect to the best stratigraphic units. We observed that production behavior of these wells is possibly driven by their lateral placement. The better producing wells are placed within the middle of the best stratigraphic units whereas the poor wells are going out the best stratigraphic units. This investigation provides a case study that demonstrates the importance of integrating datasets to identify best landing zones and the suggested workflow can be applied to other areas and reservoirs to better identify targetable zones.

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Overpressure estimation and productivity analysis for a Marcellus Shale gas reservoir, southwest Pennsylvania: A case study

et al. 2018

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It is well established that geology, geomechanics, and completion play an important role in gas productivity from shale formations. In particular, overpressured shale — along with higher fracture intensity — generally correlates with higher production rates. Conventional pore-pressure prediction techniques do not work well for black shale formations, primarily because a basin has often experienced multiple episodes of tectonic activity and subsequent overpressure generation; secondly, because the recorded petrophysical logs for pore-pressure estimation are significantly affected by the presence of organic content and gas. Overpressure mechanisms and distribution are studied for the Middle Devonian Marcellus Shale Formation of the Appalachian Basin in Pennsylvania to understand their relationship with well productivity. A sonic velocity-density crossplot does not reveal a clear picture of overpressure generation, primarily due to complex geologic history and distinct compositional variation between Marcellus Shale and overburden shale. 1D pore pressure was estimated from sonic velocity using traditional methods with pressure reference trend for the Marcellus Shale interval in the drilled wells and calibrated to the available in situ pressure measurements. 3D pore pressure was then estimated with upscaled 1D pore-pressure profiles and 3D seismic velocity data. Results suggest that the area having higher overpressure of about 160 psi produces about 55% more gas than the lower overpressure area. Further, understanding the spatial variation of overpressure in the study area may facilitate better field development planning.

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Role of Multivariate Analysis to Define the Key Driver's Governing the Well Productivity in Marcellus Shale Play

Cited by 3 publications

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Desenvolvimento de solução CRISP-DM para classificação do evento prisão de coluna no processo de perfuração de poços offshore

Desenvolvimento de solução CRISP-DM para classificação do evento prisão de coluna no processo de perfuração de poços offshore

Stratigraphic characterization of the Eagle Ford shale to identify the best landing zone: A semianalytical workflow

Overpressure estimation and productivity analysis for a Marcellus Shale gas reservoir, southwest Pennsylvania: A case study

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