The petroleum industry maintains a keen interest in asset assessment tools. This paper presents a practical case study involving high-level geological and dynamic data analysis to evaluate petroleum asset potential for further investment aimed at optimizing secondary recovery. The economic model, grounded in the balanced waterflood flow approach, determines the optimal injection volumes and the associated number of oil-producing and water-injecting wells.

Analyzing production data is complex, relying on numerous diagnostic metrics such as reserve properties analysis, reservoir energy diagnostics and watercut/GOR diagnostics, productivity measures, and economic factors. This analysis facilitates rapid modeling of future performance and forecasts economic outcomes in response to redevelopment investments.

Automation has revolutionized modern production analysis, enabling the generation of comprehensive diagnostic metrics with a simple "mouse click"—a process that typically spans months. Newly developed diagnostic metrics improve upon traditional production/injection performance analysis, especially those based on automatically generated numerical 3D micro-models that simulate expected rock/fluid properties.

Well interference is assessed through mathematical algorithms for multiwell deconvolution, utilizing extensive bottomhole pressure and surface rate data. This deconvolution, either fully or semi-automated, searches for initial pressure and unit-rate transient responses in tested and adjacent wells, aligning them with actual pressure records and aggregate flow rates.

Further advancements include the automated analysis of these diagnostic metrics, supported by AI-based digital tools that offer economic insights for enhancing production.

The case study in Western Siberia identifies deposits and wells where not all proven recoverable reserves are being tapped. It advocates for side-tracking from current wells and implementing multi-stage fracking to activate these reserves and sustain pressure. The economic model generated by this study proposes investment scenarios with a profitability index (PI) of 1.4, an attractive prospect considering the reserves’ current maturity.

The application of deconvolution in cross-well pressure interference analysis has fine-tuned production and water injection targets, yielding a 6% uplift in field oil production without the need for well interventions.

This paper presents a couple of examples of waterflooding efficiency assessment and a ranked list of investment opportunities to unlock field potential. Integrating open-hole data with meticulous well-by-well production analysis, we pinpoint prospective drilling sites. Advanced production analysis notably accelerates the analysis process, thereby diminishing the risk of overlooking enhancement opportunities.

The paper presents a study of a heavy oil mature field in Oman with aggressive water cut growth and slightly exceeding the ultimate recovery as per the initial Master Development Plan expectations. The reserves have been naturally depleted for more than a decade before trying out the waterflood a few years back. The first results of the waterflood were not consistent due to high cross-well interference from one side and possible compartmentalization from another.

The key objective of the current study was to assess the on-going waterflood efficiency, cross-well interference, possible production complications and assess possibility of improving further recovery. The key instrument of the cross-well interference analysis was based on multiwell deconvolution of the permanent downhole pressure gauges in response to the historical flow rate variations in offset wells. The water cut diagnostics was based on the large number of well-by-well metrics including recovery micro-modelling baselines, multiphase IPR analysis and multiphase productivity analysis. The mobile reserves’ potential was assessed through material balance, fractional flow analysis and decline curve analysis. Both watercut diagnostics and reserves evaluation have been facilitated by a digital assistant with a fully automated generator of numerous diagnostic metrics which otherwise would take an unrealistically long time to perform such a study.

The study has come to the conclusion that all wells are fairly connected but confirmed the deterioration of connectivity between a few wells. The water injectors have confirmed a fair connectivity with all surrounding producers while the aquifer was found to be much weaker than the effect from water injection in these wells. The study suggests that this field still contains commercial volumes of hydrocarbon reserves which can be economically recovered, preferably via horizontal side-tracks from existing wells. It has been recommended to repressurize two main reservoir units independently. The study has spotted a few suspects of thief water production and recommended reservoir-orientated production logging to locate the water source, which was most probably occurring behind the casing. These wells have been recommended as primary candidates for side-tracking.

The current study was extensively using a combination of bottomhole pressure deconvolution and advanced watercut diagnostics for heavy oil production to provide a holistic analysis of the remaining reserves. The study also provides the comparison of the results of pressure forecast between multiwell deconvolution technique (MDCV), artificial neural network (ANN) and capacitance-resistivity model (CRM).

The paper shares a practical case of production analysis of mature field in Western Siberia with a large stock of wells (> 1,000) and ongoing waterflood project.

The main production complications of this field are the thief water production, thief water injection and non-uniform vertical sweep profile.

The objective of the study was to analyse the 30-year history of development using conventional production and surveillance data, identify the suspects of thief water production and thief water injection and check the uniformity of the vertical flow profile.

Performing such an analysis on well-by-well basis is a big challenge and requires a systematic approach and substantial automation.

The majority of conventional diagnostic metrics fail to identify the origin of production complications. The choice was made in favour of production analysis workflow based on PRIME metrics, which automatically generates numerous conventional production performance metrics (including the reallocated production maps and cross-sections) and additionally generates advanced metrics based on automated 3D micro-modelling.

This allowed to zoom on the wells with potential complications and understand their production/recovery potential.

The PRIME analysis has also helped to identify the wells and areas which potentially may hold recoverable reserves and may benefit from additional well and cross-well surveillance.

The paper presents the results of a study project of 60+ well block of the large (> 1,000 wells) mature (30 year old) oilfield in Western Siberia with objective to localise and characterize residual recoverable reserves and propose the optimal economic scenario for further depletion.

Low permeability, heterogeneous reserve structure along the cross-section, numerous induced hydraulic fractures in producing wells and numerous spontaneous fractures in injecting wells with dynamic behavior, aggravated by numerous behind-the-casing crossflows in almost every well have resulted in a very complex conditions of remaining reserves.

The conventional methods of production analysis and surveillance (well testing and production logging) do not provide a consistent picture of the current distribution and conditions of the remaining reserves and required a deeper and more complex analysis.

Development Opportunities Management workflow was chosen for this particular holistic study, which includes a set of interconnected studies, field surveillance, geological and flow modelling and culminated in field development planning based on the digital asset twin. (Ganiev, B., 2021)

Digital asset twin was constructed based on results of this workflow with a full-range economical model, flow simulation over the thoroughly calibrated fine-grid 3D dynamic model and production complication model (dynamic behavior of the fractures and behind-casing channeling).

The 3D model has been calibrated on results of the cross-well pressure-pulse surveillance, reservoir-oriented production logging and was validated by the results of the drilling of the transition wells.

The digital asset twin was used to find the optimal investment scenario based on multivariate calculations with the help of digital assistants.

Due to simplicity of the user interface and client-server design, the digital twin was made available for various corporate engineers and managers without any modelling skills to play around with their own ideas on possible production/investment scenarios which gave another level of validation of the ultimate field development plan.

All activities carried out within the digital twin automatically generate a complete package of investment metrics (NPV, PI, IRR, MIRR, Cash Flow and many correlation graphs) to assess the economic efficiency of each package and select the most appropriate solution for further ultimate choice.

The approved scenario was based around drilling 6 producing side-tracks in specific locations/trajectories, performing workovers on specific offset injectors and re-scheduling of the production/injection rates in all block wells.

The results of the field development's activities implementation will be the subject of a future publication.

The paper presents a practical case of production performance analysis at one of the mature waterflood oil fields located at the Volga-Ural oil basin with a large number of wells. It is a big challenge to analyse such a large production history and requires a systematic approach.

The main production complication is quite common for mature waterflood projects and includes non-uniform sweep, complicated by thief injection and thief water production. The main challenge is to locate the misperforming wells and address their complications.

With the particular asset, the conventional single production analysis techniques (oil production trend, watercut trend, reservoir and bottom-hole pressure trend, productivity trend, conventional pressure build-up surveys and production logging) in the vast majority of cases were not capable of qualifying the well performance and assessing of remaining reserves status. The performance analysis of such an asset should be enhanced with new diagnostic tools and modern methods of data integration.

The current study has made a choice in favor of using a PRIME analysis which is multi-parametric analytical workflow based on a set of conventional and non-conventional diagnostic metrics. The most effective diagnostics in this study have happened to be those are based on 3D dynamic micro-models, which are auto-generated from the reservoir data logs.

PRIME also provided useful insights on well performance, formation properties and the current conditions of drained reserves which helped to select the candidates for infill drilling, pressure maintenance, workovers, production target adjustments and additional surveillance.

The paper illustrates the entire PRIME workflow, starting from the top-level field data analysis, all the way to generating a summary table containing well diagnostics, justifications and recommendations.