Locate the Remaining Oil ltro and Predictive Analytics Application for Development Decisions on the Z Field
- Cristian Masini (Petroleum Development Oman) | Khalid Said Al Shuaili (Petroleum Development Oman) | Dmitry Kuzmichev (Leap Energy) | Yulia Mironenko (Leap Energy) | Saeed Majidaie (Formerly with Leap Energy) | Rina Buoy (Formerly with Leap Energy) | Laurent Didier Alessio (Leap Energy) | Denis Malakhov (Target Oilfield Services) | Sergey Ryzhov (Formerly with Target Oilfield Services) | Willem Postuma (Target Oilfield Services)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17-19 September, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Analytics, Efficient, Redevelopment, Mature Fields, Decision
- 2 in the last 30 days
- 183 since 2007
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Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.
A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.
In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.
The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.
Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.
In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
|File Size||2 MB||Number of Pages||24|
Advanced Methods for Determining the Value of Information of Survelliance Data Acqusition Using predictive Analytics. Nor Idah Kechut, Abd Azim Hassan, Wan Fatin Izyan, W M Zamri, Mohd Razib A Raub and Raj Deo Tewari, Petronas; Dmitry N. Kuzmichev, Yulia Mironenko, Rina Buoy and Laurent D. Alessio, LEAP Energy. SPE-186304-MS.
LTRO Workflow for Fast Turnaround Field Optimisation Studies and Efficient Development Decisions. Cristian Masini, Sergey Ryzhov, Dmitry N. Kuzmichev, Rina Bouy, Saeed Majidaie, and Denis Malakhov. AAPG Decision Based Integrated Reservoir Modeling Workshop, 30th of October – 1st of November 2017. Includes: abstract (AAPG #90314), poster (AAPG #42191) and burst presentation (AAPG #42190).