Simulation studies are often conducted on single-reservoir levels even though hydrocarbon accumulations hardly occur in isolation. However, it is sometimes beneficial to conduct studies on multiple reservoirs to evaluate opportunities in the sequence simultaneously. This can help to optimize the development plan by enabling the combination of opportunities with multiple strings and providing an avenue to evaluate interactions among reservoirs. Where these interactions exist, they can be critical parameters in the simulation history matching process1. This approach was used in the simulation studies of five reservoirs across two fields (Field_A & Field_B). Collaboration among an interdisciplinary team helped to overcome challenges in the history matching process, especially regarding a recent observation that one reservoir was exhibiting an increasing reservoir pressure while on production with no water injection. The learnings/insights from the study were used to improve field management of reservoirs with water injection, optimize base production, and identify five development opportunities with a proposed incremental recovery of 15 MMBO.

Reservoir-H sequence, comprising of three reservoirs (H1, H2 & H3) is one of the most complex reservoirs in Niger-delta. With a combined well-count in excess of sixty producers and injectors and a production history of more than fifty-five years, the reservoir has had a history of challenging simulation studies with average water-cut matches resulting in new wells having high water breakthrough from onset. In the latest effort, an assisted history match using genetic algorithm was employed. This approach is a two-step approach including an identification of all relevant history match parameters for the three reservoirs, followed by a fine-tuning of pressure and saturation history match using genetic algorithm. This approach enabled the identification of aquifer assumptions (architecture and transmissibility) as a critical factor in successfully matching the wells in these reservoirs. In addition to pressure and saturation matches, infill opportunities were further validated by tracking current reservoir fluid contacts with the model. The current model has significantly improved the overall water-cut match in more than ten wells that historically had water-breakthrough challenges while using principally global history-match parameters. The elimination of many local changes in the current model is expected to improve both the reliability and the shelf of the model. Also, the variance between estimated contacts compared to actual gas-oil and oil-water contacts around infill locations is less than five feet indicating good predictability of the model. In order to save development cost, multiple opportunities identified in these reservoirs are to be targeted with dual strings. Additional savings were realized by reducing the overall simulations studies timeline by four months.

The reliability of dynamic simulation models can spell the difference between value creation or erosion during the development of a hydrocarbon reservoir. There is a strong need to use every available data during reservoir characterization, earth modelling and history matching of the production and pressure history of the reservoir. Of greater importance is the need to blind test the history-matched simulation model, to ascertain its reliability, especially when the model is to be used for further development of the reservoir. This paper details an offshore Niger Delta case study in which saturation logging results were used to blind test a history matched model, with an objective to further validate the model. The saturated oil reservoir was fully characterized using high resolution sequence stratigraphy and the earth model developed with available static data. History matching of the dynamic model was carried out using the parameter estimation approach, incorporating available dynamic data and tracking of contact movement observed in post-production wells. Following the history match, a saturation log was run in one of the producers in the reservoir, as a blind test for the quality of the history match. Results of the log matched the contacts in the dynamic model within 1 ft, in the subject well, providing additional confidence in the quality of the model. As a result, matched model has been used for the maturation of 2 new drill opportunities with significant estimated recoveries.

Design of experiments (DoE) is an industry methodology designed to test the impact of different variables on an objective function without running every single scenario possible. Despite its many advantages, there still exists the tendency to engage in re-work, particularly when the results of the experiments do not align with expected outcomes. This paper highlights the need for a decision-based approach for DoE studies to eliminate inefficiencies in resource utilization. A decision-based process focuses changes in variables and analyses on those that have the potential to impact the final decision for which the study is being carried out. A DoE study for a virgin reservoir development, conducted in two run cycles is used to highlight this. For the first cycle, the top variables from stakeholder engagement and initial Pareto and Tornado analyses were carried forward for final runs and analyses. The same was repeated for the second cycle, with a change in the distribution of the key variables, within the limits of analog data available. The analyses of both run cycles show close alignment of the results of both run cycles. Also, the same development decision was reached from the results of both run cycles, even though changes were made to the input variables for the second run cycle. A review of the Tornado chart for either run cycle shows that the development decision can be arrived at with either one, without the need for the other. This shows that the number of run cycles for similar studies can be minimized to ensure that only those that have the potential to change the decision to be made are carried forward. This will ensure optimum use of the limited available technical, time and computational resources, allowing the professional to do more with less.

In the Early phases of field development, the drilled hydrocarbon appraisal wells may not have been sufficient to define rock properties, fluid typing and contacts. It's very important to define the range of uncertainty in such fields. This is because as the field matures other dynamic data will become available to validate these probable volumes. The ideal development scenario provides the practitioner with a full suite of data defining the reservoir geometries, reservoir properties, fluid properties etc. to make subsurface decisions. However, in most cases, operational realities will deny the reservoir practitioner this full suite of data. One practical convention that is used to resolve this data paucity challenge is to evaluate and report the lowest possible volume, if this low case is economic the project will be economic with potential for more upside outcomes. However, a challenge that can arise with this is that after several iterations the low case can become the only case. A better practice is to characterize uncertainty of reservoir parameters during the early stages of field development and carry out the full range outcomes through the field's life. These ranges will then be validated as the field matures. This paper demonstrates a case in the Niger Delta field A05 reservoir were dynamic simulation model was used to narrow the uncertainty range on the GOC. Proper identification and characterization of the GOC uncertainties helped for the estimate of a range of STOOIP used for dynamic simulation model. Though no static dataset was available to reduce this uncertainty on the GOC, during dynamic simulation, the high-case oil in-place volume was found to be the best match to historical production data with the integration of another reservoir, Delta A12, in one dynamic simulation model. Both reservoirs communicate through the aquifer, separated by a saddle. This then proved up additional volumes in the reservoir, identified previously overlooked reserves and allowed the asset team to propose an extra infill well opportunity than what was previously planned. This new understanding of the A05 reservoir increased the oil estimated ultimate recovery (EUR) by 4.6 MMSTBO.