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In stacked reservoirs with commingled production, achieving an understanding of relative contributions of the flow units is fundamental to reservoir management, most notably for conformance management of reservoirs under water flood or enhanced oil recovery (EOR) scheme. To that effect, the desired surveillance data usually includes: reservoir layer pressures, phase distribution profiles (through PLTs) in flow units, and monthly well test data (water cut, gas oil ratio, oil rate etc.). These measurements will form the basis of well by well flow unit production allocation; all necessary information for classical engineering analysis and reservoir simulation. The enduring challenge of value-effective reservoir management is to determine the ‘adequate’ frequency and selection of well and flow units data acquisition. Industry practice shows clearly that there is no consistent answer to this challenge. In the authors' opinion, this is due to the unavailability of a methodology and tools to rigorously define the Value of Information (VOI) associated with surveillance data acquisition. VOI is defined as the net present value (NPV) difference between the total production & costs outcomes with the benefit of information, and the total production & costs outcomes without this information. In some cases, the VOI can also indirectly translate to critical understanding of subsurface integrity such as unintentional communication of deeper, higher pressure gas reservoir with shallower reservoir units having a much lower fracture gradient that if left unattended could subsequently lead to subsurface blowout scenario. In this paper, we set out to define surveillance data acquisition decisions as an optimisation problem: where is the optimum cost versus reward for a field, given allocated well production and the usual (partial) understanding of reservoir layer absolute and relative permeability at the well, from logs and core. We present how novel predictive analytic algorithms, coupled with multi-phase deliverability models, material balance analysis, and global optimisation search methods are integrated to assess the resulting uncertainty in layer-phase allocation, in presence of different surveillance datasets. We use a representative synthetic field simulation model to formulate reservoir outcomes. As a precursor to a full VOI, we define an allocation uncertainty versus the data acquisition frequency, and provide general recommendations in terms of data frequency and type that can be generically used as findings.

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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.