Steam flooding as a tertiary recovery method for recovery of oil from heavy oil reservoir has been of interest in recent years. Analytical models are very useful to predict oil recovery by steam flooding for preliminary forecasting purposes and sensitivity studies. Though different models are available, the predicted values did not satisfy the field value because of presumptions. In the present study, an attempt has been made to modify the existing Jeff Jones model and Chandra and Mamora model by considering the true profile of steam zone size in reservoir and vertical sweep efficiency for calculation of capture efficiency. The reservoir characteristics and production data of three oil fields, viz., Schoonebeek in the eastern part of Netherlands, San Ardo in Monterey County, California, USA and Hamaca in Venezuela's Orinoco heavy oil belt were analyzed for performance prediction of oil production. The modified model gave very satisfactory results for production performance, compared to the original Jeff Jones and Chandra and Mamora model.

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For planning the operations of Oil and Natural Gas Corporation Limited (ONGC) in the complex Heera field, it was estimated that over one hundred simulation runs would be needed to complete the history match of the field and almost the same number of simulations would be needed for production forecasting. Heera is a large field, with multiple faults and seven stacked carbonate formations. There are significant variations in petrophysical properties, and variable degrees of communication between reservoir zones. The simulation models include 479 wells with commingled production or injection. Well trajectories are complex and include multilateral and horizontal configurations. Field development options include use of simultaneous water alternating gas (SWAG) for enhanced oil recovery. Combining all these features, it would be difficult to run all the necessary sensitivity cases within the required project timeline, using a conventional reservoir simulator. Therefore, it was decided to test the applicability of a new generation simulation tool to address the challenges of the study. To ensure that the change of simulator would not impact the integrity of the model, rigorous quality checks were performed on the input data. After successful evaluation, the new software was used for the reservoir engineering study. The decision to apply the new simulator significantly reduced the elapsed time, with some realizations over 20 times faster compared to the original base case. As a result of this speed-up, numerous runs could be carried out to refine the history match. Multiple sensitivities could be used to help understand and reduce the uncertainties in a more comprehensive manner. Moreover, the prediction cases could be optimized to identify the best recovery strategy. This study has demonstrated the value of reducing simulation run times, to complete the project with greater efficiency and more confidence in the results. In future studies, high performance software tools can also enable use of fine resolution models, to capture detailed heterogeneities and optimize areal and vertical sweep.

Hydraulic fracturing is a widespread well stimulation treatment in the oil and gas industry. It is particularly prevalent in shale gas fields, where virtually all production can be attributed to the practice of fracturing. It is also used in the context of tight oil and gas reservoirs, for example in deep-water scenarios where the cost of drilling and completion is very high; well productivity, which is dictated by hydraulic fractures, is vital. The correct modeling in reservoir simulation can be critical in such settings because hydraulic fracturing can dramatically change the flow dynamics of a reservoir. What presents a challenge in flow simulation due to hydraulic fractures is that they introduce effects that operate on a different length and time scale than the usual dynamics of a reservoir. Capturing these effects and utilizing them to advantage can be critical for any operator in context of a field development plan for any unconventional or tight field. This paper focuses on a study that was undertaken to compare different methods of simulating hydraulic fractures to formulate a field development plan for a tight gas field. To maintaing the confidentiality of data and to showcase only the technical aspect of the workflow, we will refer to the asset as Field A in subsequent sections of this paper. Field A is a low permeability (0.01md-0.1md), tight (8% to 12% porosity) gas-condensate (API ~51deg and CGR~65 stb/mmscf) reservoir at ~3000m depth. Being structurally complex, it has a large number of erosional features and pinch-outs. The study involved comparing analytical fracture modeling, explicit modeling using local grid refinements, tartan gridding, pseudo-well connection approach and full-field unconventional fracture modeling. The result of the study was to use, for the first time for Field A, a system of generating pseudo well connections to simulate hydraulic fractures. The approach was found to be efficient both terms of replicating field data for a 10 year period while drastically reducing simulation runtime for the subsequent 10 year-period too. It helped the subsurface team to test multiple scenarios in a limited time-frame leading to improved project management.

Objectives/Scope Rock fabric characteristics of Gamij Field lies in the purview of conventional reservoirs but are as complex and uncertain as unconventional. It is a multi-layered, heterogeneous reservoir on depletion drive with very low permeability. Even after hydraulic fracturing and artificial lift, the production rate lies in the range of 3-4 m3/d. This paper evaluates the impact of past hydraulic fracture operations and uses this understanding to optimize the stimulation strategy for future wells. Methods, Procedures, Process A customized multidisciplinary modeling and flow simulation workflow; integrating petrophysical, geomechanical, stimulation and production data was adopted and applied to sectors of the field. Two techniques were combined 1. Unconventional (Fast Loop) 2. Conventional (Slow Loop) in an intriguing and iterative manner. Hydraulic Fractures were designed, optimized and calibrated using a rigorous workflow of unstructured grid and unconventional fracture modelling/3D planar fractures in the sector models. Sector model is considered the most effective approach to characterize completion quality in Gamij Field due to the limitation of current modelling technologies to design and simulate hydraulic fractures in full-field model. Results, Observations, Conclusions The results of sector model is validated with full field model and a number of iterations were performed to match pressure from the result to the initially assumed in creation of 3D MEM (Mechanical Earth Model). Reservoir quality (RQ) estimation is affected by complex mineralogy including abundance of iron and titanium rich sediments. Stress regime shows vertical transverse isotropy nature of shales and suggest re-orientations near to fault zones. There are several areas, especially in the eastern part, where the tectonic regime changes from normal to strike-slip faulting. HF modelling not only explains the contrasting behavior of existing wells, but also discusses alternatives that could help to unlock the true potential of the pay zones. This paper elucidates techniques to maximize reservoir understanding and allow optimization of hydraulic fracture design in terms of casing diameter, job size, and design. Simulations shows multiple fractures were created from different preformation cluster in a single stage treatment. Overall, the case study showcases different factors that govern the development of a tight oil reservoir and the ways to characterize and quantify these uncertainties. Novel/Additive Information This work is the first step to quantify the complex reservoir mineralogy, impact of laminations, depletion, stress variation on the efficiency of HF jobs. Identification of potential sweet spots based on reservoir quality and completion quality indexes, establishing well productivity. The uncertainty cannot be eliminated but it ought to be reduced and risk analyzed before the actual execution.