An opportunity to establish a certain level of confidence in the well models by analysing entire production history or even performing analysis in real time becomes a reality of today. This paper is intended to describe an engineering approach to the analysis that was tailored to the Al Shaheen field to better understand the well performance, gain confidence in the models and identify various well issues and opportunities. The challenge of understanding how wells perform is always associated with comprehensive data mining and significant time spent on analysis and calculations. However the data available is always limited and often requires quite a few assumptions to be made by an engineer when building a representative and reliable well model. All industry standard software packages utilise same or similar well-known concepts and types of analysis from simple equations to more comprehensive algorithms. These are like pieces of the puzzle that can be assembled together to help petroleum engineer to get an idea of how the well should perform in particular circumstances. The way petroleum engineers applying these concepts on daily basis may vary depending on the nature of the problem they are facing and the amount of data they have available. Quite often the fact that the model does not match the reality is used to invalidate existing data and an opportunity to understand that something is happening in the well that is not captured by the model is overlooked. Reasons for this may include an existence of a particular purpose of the well model, level of engineer's experience, skills or imagination, lack of required data or at the end an inability to process the entire production data in an efficient way. The last becomes a real challenge on the fields with large amount of wells and extensive production history. Synergy between adopted analysis and the technology has allowed engineers to gain a much better understanding of the well performance, identify various issues and opportunities and enabled them to keep focus on making decisions as to which wells to optimise and those to troubleshoot to maximise potential of the existing well stock.

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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.