Masini, Cristian (Petroleum Development Oman) | Al Shuaili, Khalid Said (Petroleum Development Oman) | Kuzmichev, Dmitry (Leap Energy) | Mironenko, Yulia (Leap Energy) | Majidaie, Saeed (Formerly with Leap Energy) | Buoy, Rina (Formerly with Leap Energy) | Alessio, Laurent Didier (Leap Energy) | Malakhov, Denis (Target Oilfield Services) | Ryzhov, Sergey (Formerly with Target Oilfield Services) | Postuma, Willem (Target Oilfield Services)
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.
Al Fadl & Al Qadr fields are located in the eastern part of the Abu-El-Gharadig Basin (Western Desert, Egypt). The exploratory wells Al Fadl -1 & Al Qadr -1 were drilled late 2007, encountered under saturated oil in the Bahariya Formation (Cretaceous). The development lease was granted in January 2008 after the successful testing of the wells. Production started in April 2008. These discoveries offered an attractive opportunity to increase Bapetco's oil production. Due to its location away from the existing Bapetco facilities, Early Production Facilities were installed to enable production to start just after the development lease was granted. The development required an integrated and x-asset planning approach to accelerate its development and maximize hydrocarbon production without compromising other development. The main strategy was to fast-track maturation and appraisal/development opportunities in the area. The main aim was to expand Bapetco's operations and achieve early production, and prepare for the secondary recovery (water flood scheme). The Petroleum Engineering studies consisted in the construction of comprehensive 3D models of the marginal marine reservoir sequences capturing key uncertainties. The static models were exported to dynamic simulators. Geological, petrophysical and reservoir engineering data were integrated to create realizations reflecting extreme scenarios for reservoir parameters such as reservoir architecture, structure and fluid contacts in an attempt to define the in place hydrocarbon volume range, the static connectivity and to test the robustness of the development concepts. Dynamic models were used to provide forecasts for proposed realizations. Very successful multidisciplinary integrated study work resulted in fast track maturation of FDP and delivery of 27 well proposals within 6 months. The development scenario selected consists of an inverted 5 spot pattern (spacing between well producers of 600m, fracced wells, ESP completion). Currently the fields are producing through the EPF system while development drilling is delivering 2 producers a month. Water injection is planned to start in the Q1 of 2010.
The JG field is located in the North East Abu Gharadig (NEAG) Basin of the Western Desert in Egypt. With first production in 2002, it is the first commercial discovery in the
Middle Jurassic Lower Safa Reservoir Units in this basin. Oil and gas are produced from the tidally influenced estuary channel deposits in the Lower Safa A Unit and oil from the massive braided fluvial channels in the Lower Safa C Unit.
At first, the field was believed to consist of one single hydrocarbon column. However based on production behavior and additional well information it became apparent that the
field was highly compartmentalized in the vertical and horizontal domain. Since then multiple data sources have been leveraged in order to obtain better compartment definitions: 3D seismic, logs, PVT data, geochemical fingerprinting, repeat pressure surveys and production data.
The boundaries between the reservoir compartments are defined by a combination of faults and stratigraphic heterogeneities. Although clear in places, some compartment
boundaries can only be defined from non-geological data sources. Understanding these heterogeneities and compartment boundaries is essential for optimizing the field development.
Like so many fields the JG field proved to be more complex than initially expected. It is argued that extensive data gathering, in particular in the early field development, is essential in helping to timely identify and properly define such complexities.