Historically, field development plans are determined by reservoir production profile generated in standalone subsurface models using a simulation-based forecast. The resulting oil production profile is commonly over estimated because the constraints imposed by the infrastructure capacity on the surface network are not captured appropriately. Moreover, reservoir simulation engineers are forced to apply many simplifications to represent the backpressure impact of topside facilities over the reservoir deliverability. A common practice is to constrain the model, assigning the same minimum bottom hole pressure or well head pressure to all wells and thus misrepresenting the actual wells' behavior. In general this approach may lead into poor development plans, suboptimized system designs, incorrect budget estimation, over expenditures, and so on; all of these issues will impact the overall asset performance and hydrocarbon recovery. Integrated asset modeling (IAM) is a holistic approach that allows upstream and downstream components to be modeled together in order to accomplish a comprehensive production forecast on truly surface constraints. This paper contains a smart field case that demonstrates how coupling subsurface and surface models can effectively improve production forecast accuracy and leverage production optimization from well to asset level in order to provide decision support that takes into account the complexities of interactions between upstream and downstream domains. This work addressed problems such as: the impact of fluid composition variation over time, the interdependencies between wells sharing facilities, the effect of the production strategy or reservoir guidelines over total field production when multiple reservoirs are connected to a surface network sharing common capacity constraints, and optimization of the artificial lift strategy in place. After economical evaluation, it was possible to redefine a development plan considering surface constraints and actual production profiles that optimally extent production plateau for the next 10 years. The value of having an IAM is that it allows for the evaluatuation of the relative impact of downstream and upstream interdependencies over time, thus enabling comprehensive assessment of a wider range of scenarios and development opportunities. This is turn shall contribute to maximize asset profit and generate opportunities to sustain production based on economic and financial indicators.

The studied field is located in Abu Dhabi and has been identified as faulted anticlines structure with associated local fractures. The reservoir is characterized by lateral and vertical variations in reservoir rock and fluid properties ranging from a gas condensate at the top of the structure to undersaturated black oil down the reservoir. A substantial compositional gradient has been identified from over 50 fluid samples taken at different depths over the thick fluid column of about 1500 feet. The field is presently under miscible hydrocarbon WAG (HC-WAG) injection, water injection and future consideration for CO2-WAG injection.The field has been characterized using an Equation of State (EOS) model and compositional mapping with areal changes of saturation pressure. Therefore, the aim of this study is to improve the compositional model that can replicate the areal and vertical variation in the fluid thermodynamic. This presents a huge task considering the vast number of laboratory experiments. Hence, the updated EOS will be used to improve the reliability of reservoir modeling, production forecasts and operational requirements.All the fluids analyses studies were compiled and validated. These data were analyzed areally and vertically down the fluid column. Correlations of saturation pressure, gas-oil ratio, formation volume factor and compositions were generated with depths across the formation. The data showed a large variation of saturation pressure, all fluids parameters and composition with depth especially close to the gas oil contact. Then the compositions and saturation pressures were validated using a 1-D vertical model and obtained results were compared with the experimental values at various depth.The obtained result proved that a single EOS and fluid representation could model the complex thermodynamic behavior of all the reservoirs in the field. Considering that part of the field has been under HC-WAG injection and future consideration for other WAG processes, the generated EOS would allow the numerical simulation to have a better representation of the fluids and predict the impact of these processes on the ultimate oil recovery.

Achieving history match for all three phases in a surface network model is not a straight forward task. Many variables are interacting in the process from sandface, well models, system architecture, measurements, and well testing until export meters. The objective of this study is to illustrate a methodology followed in a smart field located in Abu Dhabi, where it was possible to calibrate and match oil, gas, and water historical production rates in a top side model containing more than 55 strings among producers and injectors. Reservoir characterization played a very important role since the asset contents - a series of carbonate oil baring reservoirs with different properties - connected to a common surface network. The main reservoir has a strong compositional gradient starting from gas condensate to black oil fluids with saturation pressure and GOR varying in a wide range. Wells associated with the most prospective zone are placed in five inverted patterns as part of a miscible alternate water and gas injection (WAG) process which provide pressure support; others are under peripheral water injection with very stable bubble pressure and GOR across all areas. To combine in a single model all the complex and different fluid characteristics and dynamic interactions represented a challenge for the engineers. A surface network model was built with the purpose of creating online production optimization scenarios that consider all production system behavior using an economical model for boosting oil while minimizing gas production in order to reduce operating costs. After network calibration was performed, oil and water total rates matched. However, the resulting gas production was overestimated compared with the one reported in export meters, indicating that fluid properties need to be tuned since important compositional changes were expected to happen when pressure drops from subsurface to the processing plant. The present study demonstrates how to construct a representative fluid model for a group of complex reservoirs that honor actual production rates for all phases allowing network model history match, reducing computing time to accomplish comprehensive production optimization based on truly surface constraints.

The energy industry, including the new focus on geothermal and carbon sequestration processes, deals with porous and permeable formations. Under the influence of effective stress, these formations undergo elastic and inelastic deformation, fracturing, and failure, including porosity and permeability changes during production. Grain and Bulk moduli of elasticity are two key parameters that define net effective stress due to partitioning of stresses between the pore pressure and grain-to-grain contact stresses. Effective stress explains poroelastic behavior; however, tight rock behavior under in-situ conditions is still not predictable. This paper proposes a new method, which uses formation evaluation (FE) measurements, and an integration of rock physics and geomechanics concepts, to constrain effective stress in tight rocks. Examples are presented demonstrating the usefulness of the work. Effective stress (σ′) is expressed as the difference between total applied stress (σ) and pore pressure multiplied by Biot’s coefficient (α). The ‘α’ for highly porous rocks is unity where applied load is counteracted equally by grain-matrix and pore-pressure. However, for tight rocks, only a fraction of load is shared by pore fluid and the ‘α’ is much smaller than unity. Biot’s coefficient ‘α’ is expressed in terms of bulk modulus (Kb) and matrix modulus (Kma). Kb is estimated from acoustic logs as well as measured by hydrostatic compression tests in the laboratory. However, Kma is much more difficult to measure safely and economically, especially in tight or very low permeable formations, and as such, the common practice is to estimate it theoretically. A simple and clear methodology is proposed to estimate Kma from FE logs as well asX-RayDiffraction (XRD) mineralogy obtained from formation core and drill cuttings. Kma can be constrained by an upper-bound (Voigt, 1910), a lower- bound (Reuss, 1929), and an average of the two, (Hill, 1963) models. Kb, on the other hand, can be reliably estimated using dynamic acoustic wave velocity and the static equivalents calculated during calibrations from core tests under net effective in-situ stress conditions. The Kma and Kb, thus obtained, will give a good estimate of Biot’s coefficient ‘α’ in tight rocks. The work provides an improved estimate of net- effective-stress in tight rocks, which leads to safety and cost savings through better prediction of drilling rates, hydraulic fracture design and production decline. The work also examines a new method in which Kma could be estimated by weight fraction of minerals.

Finding an environmentally innovative yet a commercially viable solution to meet the growing energy demands is becoming more challenging with time. CO2-EOR forms an integral part of ADNOC's de-carbonization strategy and in view of its reported advantages, a series of CO2 pilot and projects were implemented. Based on the gained experiences, a business case was generated to convert a field from HC-WAG to CO2-WAG; looking for win-win situation for the environment and HC extraction. The currently implemented development plan relies heavily on HC-WAG injection and has been facing several challenges including, maintaining miscibility conditions and UTC optimization efforts by gas curtailment. In this study, subsurface and surface assessment of alternative field development scenarios was conducted; aiming to convert the field to CO2-WAG. The study was initiated by evaluation of reservoir performance to identify areas for improvement and accelerate the decision making process. This was later incorporated into a dynamic model via diverse set of field management logics to screen wide range of scenarios. The simulation results were analyzed using standardized approaches where a number of key indicators was cross-referenced to produce optimal field development scenarios with regards to CO2-EOR effect on the reservoir, understanding CO2 efficiency post HC flooding, miscibility conditions, balanced pressure depletion, harmonized sweep as well as robust reservoir engineering ground. The optimal scenarios were assessed with in-house engineering, in line of having strong economic indicators, honoring operational constraints, corporate business plan and strategic objectives. The study is unique and one of very few cases available in literature to highlight shifting field development with an established history of HC-WAG injection to CO2-WAG. The methodology applied in this study uses an integrated subsurface-surface structured approach to tackle reservoirs challenges related to CO2 Conversion, generate alternative option to showcase the benefits of CO2-EOR as an environmentally friendly solution.