Results

Maturing giant and super giant fields have, typically, an extensive data set ranging from seismic data to time lapse surveillance data. The data set, associated studies and models together with driving values defined by ADNOC form the foundations of long-term plans, field development plans, business plans, reservoir management plans and production optimization plans. Ensuring the adequacy and optimality of the plans and their capability to meet their prescribed objectives is a very challenging task that requires unique assessment workflows. ADNOC is undertaking multiple fast-tracked Integrated Reservoir Performance and Production Sustainability Assurance (IPR) projects with the above objectives in mind. In this paper we will share the experience gained through the execution of such projects and the way this experience helped refining the workflows and the associated value.

We designed and applied unique workflows that combine "Bottom up" approaches by technical discipline with the "Top down" focusing on those factors that have the largest impact on the scope of the plans and the ability to deliver the expected outcomes. The identified issues and opportunities are presented in terms of their impact on volumes in place, reserves, facility, drilling plans, surveillance plans, modeling, etc. and are associated urgency indicators to help prioritizing actions.

The adopted integrated methodology and workflows helped in identifying and ranking various issues related to the reservoir models (static and dynamic) and many recommendations on how to tackle these issues in the new generation models were provided. Advanced reservoir management workflows were generated towards optimal production and injection balancing as well as to better manage the water flood and identify the most offending injectors. Many scenarios were explored to check the different elements of the full field development plan and ongoing projects, considering all the identified uncertainties. Many recommendations were provided, accordingly, concerning infill drilling and future gas lift program. Specific workflows were generated to optimize the performance of the existing gas lift wells and to identify and rank the future wells that will need gas lift according to their urgency, hence confirm the gas lift compression capacity that was subject of an ongoing project.

Key enabler to complete the project in the planed time frame was the use of cutting-edge modeling technology which has a drastic impact on the project and the team's capability to explore a comprehensive set of scenarios with associated sensitivities and uncertainty analysis providing unique insights towards more optimal decisions and clearer way forward.

Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management.

The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset.

This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance.

Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction.

When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21^st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.

Reservoir A is being developed in early and interim phases in order to gather static & dynamic data to minimize the risk associated to subsurface uncertainties. In early and interim phases, only production is taking places. During full field, water injection scheme will be implemented using mainly 5-spot pattern. It is very crucial to measure the subsurface uncertainties and their impact on the reservoir development. For this purpose, the uncertainty parameters are identified and their ranges are selected based on the current well performances during probabilistic History matching (PHM) phase. In full field runs, the uncertain subsurface parameters are quantified to prioritize the future reservoir monitoring and data gathering plans. Note that wells are equipped with the permanent downhole pressure gauges.

Reservoir A is one of the major reservoirs of a green-field located offshore Abu Dhabi and is being developed with a 5-spot water injection pattern. The producers and water injectors are horizontal wells which are drilled across different flow unit within the reservoir. The reservoir properties are variable across all the flow units, which may results in a non-uniform water front. Being a green field, there are more uncertainties as compared to the brown field. More than three years production & pressure data is available which is used in this uncertainty study. This production data is mainly used to achieve the probabilistic History match on well-wise basis. In this uncertainty study, previous HM parameters are removed. However, based on previous history matching learnings, the subsurface uncertain parameters ranges are selected for this probabilistic History match phase. The criteria for filtering the valid runs during this phase are set to be ±150 Psi compared to the actual downhole pressure readings. In case of decreasing this filtering range to 75 Psi, results in reduction in the reserve range in P90 to P10. Based on ±150 Psi principle, the subsurface parameter ranges are furthered reformed for full field uncertainty study/run. The industry standard workflow is followed to quantify the subsurface parameters during this phase. In this study, we used the Permeability modifiers based on RRT, Faults transmisibilities, Relative Perm curves (based on SCAL data), Kv/Kh ratio (from PTA), etc. as uncertain parameters. The impact of each parameter is measured and quantified with respect to plateau and total reserves.

The workflow is implemented for designing Lower completion with inflow control devices &/or inflow control valves (ICD/ICV) for high departure long horizontal wells in a Green Field located North West offshore Abu Dhabi. The major challenges that being faced in the field development include reservoir heterogeneity with high permeability contrast ranging from 0.1 to 500 md, fault network and high uncertainty about Tar Mat surface & Oil Water contact. Main objectives of ICD/ICV completions are; to have uniform influx/flow profile from all sublayers of reservoir by dividing horizontal drain in compartments based on reservoir properties variations, minimize heel to Toe effect, controlled inflow from high permeability streaks, without compromising total well deliverability; most importantly to encourage more inflow from the lower permeability regions. An appropriate reservoir sector model having one deviated gas injector, one/two horizontal water injector(s) and one ICD/ ICV candidate oil producer was extracted to be used for this study. Single time step static modelling and dynamic sector modeling simulation approaches were implemented for ICD/ICV modeling.

When a rock is fractured, its capillary pressure may radically change. In other words, the effective capillary pressure of the fractured rock is no longer the same as that of its original matrix. This is an important phenomenon to consider when building static and dynamic models of fractured reservoirs. Fracture capillary pressure can have a significant impact on modeling of initial water saturation. In a reservoir model, initial water saturation is typically calculated as a function of capillary pressure. While matrix capillary pressure can be obtained by measurements of core plugs, fracture capillary pressure is hardly known from actual data due to limited or no measurements from fractured cores. In this case, a practical solution to assign capillary pressure in a model is to modify the matrix capillary pressure in a reasonable manner. Generally, matrix of a tight (low porosity) rock can have very high capillary pressure, but once it is fractured, the water saturation in the fractured rock should be dramatically decreased. If we ignore this phenomenon, water saturation will be significantly over-estimated and consequently oil-in-place is under-estimated. Another impact of the incorrect capillary pressure can be on dynamic models, in which the model will show significantly early water breakthrough and dramatically higher water cut than field data. Therefore, correctly modeling fracture capillary pressure is critical for both static model building and dynamic model simulation. The proposed solution is to assign the effective capillary pressure for fractured rocks independently from non-fractured ones. The solution has been applied in a couple of giant carbonate offshore reservoirs in the United Arab Emirates and has demonstrated significant benefits in reservoir models with improved fluid stability and better water cut matches.

The field of data-driven analytics and machine learning is rapidly evolving today and slowly beginning to reshape the petroleum sector with transformative initiatives.

This work describes a heuristic approach combining mathematical modeling and associated data-driven workflows for estimating reservoir pressure surfaces through space and time using measured data. This procedure has been implemented successfully in a giant offshore field with a complex history of active pressure management by water and gas.

This practical workflow generates present-day pressure maps that can be used directly in reservoir management by locating poorly supported areas and planning for mitigation activities. It assists and guides the history matching process by offering a benchmark against which simulated pressures can be compared. Combined with data-based streamlines computation, this workflow improves the understanding of fluid flow movements, help to identify baffles and assists in field sectorization.

The distinctive feature of this data-driven approach is the unbiased reliance on field-observed data that complements complex modeling and compute-intensive schemes typically found in reservoir simulation. Conventional dynamic simulation and the tracing of streamlines require adequate static (e.g. permeability tensor) and dynamic models (e.g. pressures for each active cell in the system).

Alternatively data-driven streamlines are readily available and calibrated.

This paper presents innovative algorithms and workflows to the relatively limited existing body of literature on data-driven methods for pressure mapping.

In this case study, new insights are effectively revealed such as inter-reservoir communication, enabled a better understanding of the gas movement and supported the change in production strategy.

The paper is organized as follow. After a general overview of the field studied, this paper describes in detail the workflows used to interpolate pressures in space and time along with cross-validation results. Various applications of the pressure predictions are presented in the sections thereafter.

The workflow is implemented for designing Lower completion with inflow control devices &/or inflow control valves (ICD/ICV) for high departure long horizontal wells in a Green Field located North West offshore Abu Dhabi. The major challenges that being faced in the field development include reservoir heterogeneity with high permeability contrast ranging from 0.1 to 500 md, fault network and high uncertainty about Tar Mat surface & Oil Water contact. Main objectives of ICD/ICV completions are; to have uniform influx/flow profile from all sublayers of reservoir by dividing horizontal drain in compartments based on reservoir properties variations, minimize heel to Toe effect, controlled inflow from high permeability streaks, without compromising total well deliverability; most importantly to encourage more inflow from the lower permeability regions. The ICD/ICV Completion design workflow utilized in the industry and available in literature was followed along with new improved & integrated approach of dynamic simulation modelling. An appropriate reservoir sector model having one deviated gas injector, one/two horizontal water injector(s) and one ICD/ ICV candidate oil producer was extracted to be used for this study.

This paper presents a case study of full-field simulation model history matching by applying geological concepts for various types of high-permeability (high-K) streaks.

An Upper Jurassic reservoir in "Field-A" is domal shape structure and reservoir fluid is under saturated with bubble point. Field-A has been producing oil from the reservoir for 30 years with total 20 oil producers, by evolved through the secondary recovery stage, such as crestal auto gas injection (non-miscible gas), then shifted to crestal water injection to maintain reservoir pressure. The fluid monitoring results and water-cut/GOR evolution trend indicate complexity and heterogeneity of the reservoir.

Two type of high-permeability streaks associated with stromatoporoid lithofacies in the upper part and fault related fractured dolomite in the lower part of reservoir were identified through the reservoir characterization studies. The zones having the high-permeability streaks show higher well-test KH compared to the core plug based matrix KH. Model KH of the zones including high-permeability streaks were conditioned by maps of the excess KH with the following concepts:

Stromatoporoid lithofacies:

Excess KH map was generated based on the relationship between well-test KH and stromatoporoid lithofacies thickness.

Fault related fractured dolomite:

Core observation indicates high density of open fractures is detected in dolomite of specific layers in the lower part of reservoir. Excess KH map was generated based on the relationship between excess KH and fracture density distribution estimated by 3D seismic dip attribute (fault location dependent)

Through model history matching, stromatoporoid thickness distribution in-between well control points were adjusted to modify excess KH map as one of the major uncertain parameter in the global level matching phase, considering its significant impact on pressure communication between crestal gas/water injectors and flank oil producers at overall area and history period. When it comes to local area (well-by-well level) matching phase, independent fine tuning process of excess KH maps derived from two different high-permeability streaks allowed us to achieve good matching quality for pressure, water-cut and GOR, especially for multi-lateral completion wells where injection water/gas breakthrough was occurred at different timing at each horizontal branch. The permeability modification along with the geological concepts of various high-permeability streaks contributed to achieve reasonable quality of history matching without extreme and un-realistic adjustment of the permeability model.

This paper introduces the history matching process of commingled wells and demonstrates how to reduce the uncertainty of the vertical permeability ratio (kV/kH) by analyzing the production logging results of horizontal wells which have been used only for allocation through the evaluation of horizontal well performance.

The field discussed in this paper is an offshore carbonate oil field located to the Northwest of Abu Dhabi. In this field, a lot of horizontal water injectors have been drilled and completed for the purpose of reservoir pressure maintenance. Historically, most of horizontal water injectors were completed as comingled injector covering two layers isolated by thick dense layers. Due to the complexity of the well and the difficulty of well monitoring, the well performance of these horizontal water injectors was not fully integrated into the model history matching process.

The kV/kH ratio is one of the important parameters for analyzing fluid movements in carbonate oil reservoirs. Generally, the value obtained from core analysis results is utilized for reservoir simulation models. In this paper, the effectiveness of analyzing production logging results for commingle wells is introduced as a method for evaluating kV/kH on the simulation model. The end result can reduce uncertainties of kV/kH resulting from heterogeneity of carbonate reservoirs.

It is confirmed that production logging results of commingle horizontal wells are valuable for evaluating kV/kH to be defined the simulation model. They can help to reduce the uncertainty in kV/kH, although there is not enough data for the history matching process.