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Optimized geomodeling and history matching of production data is presented by utilizing an integrated rock and fluid workflow. Facies identification is performed by use of image logs and other geological information. In addition, image logs are used to help define structural geodynamic processes that occurred in the reservoir. Methods of reservoir fluid geodynamics are used to assess the extent of fluid compositional equilibrium, especially the asphaltenes, and thereby the extent of connectivity in these facies. Geochemical determinations are shown to be consistent with measurements of compositional thermodynamic equilibrium. The ability to develop the geo-scenario of the reservoir, the coherent evolution of rock and contained fluids in the reservoir over geologic time, improves the robustness of the geomodel. In particular, the sequence of oil charge, compositional equilibrium, fault block throw, and primary biogenic gas charge are established in this middle Pliocene reservoir with implications for production, field extension,and local basin exploration. History matching of production data prove the accuracy of the geomodel; nevertheless, refinements to the geomodel and improved history matching were obtained by expanded deterministic property estimation from wireline log and other data. Theearly connection of fluid data, both thermodynamic and geochemical, with relevant facies andtheir properties determination enables a more facile method to incorporate this data into the geomodel. Logging data from future wells in the field can be imported into the geomodel allowingdeterministic optimization of this model long after production has commenced. While each reservoir is unique with its own idiosyncrasies, the workflow presented here is generally applicable to all reservoirs and always improves reservoir understanding.

Waterflooding in deepwater reservoirs typically involves injecting seawater or produced water from the surface via pumps into injection wells. This technique is often cost-prohibitive for many reservoirs and poses significant mechanical/operational risks. This paper discusses how one Gulf of Mexico (GOM) operator overcame all these challenges using smart well technology to implement the first controlled dumpflood in deepwater GOM and boosted the injection rate, reservoir pressure, and recovery from a reservoir at a depth of 20,000 ft.In a typical dumpflood project, uncontrolled water production from the aquifer and subsequent injection into the target zone occurs downhole within the same wellbore. Therefore, typical surface and downhole complexities associated with conventional waterflood projects can be avoided. In this first deepwater GOM controlled dumpflood well, the controlled water flow (≥20,000 bbl/d) is directed from the source aquifer to the target oil zone via inflow control valves (ICV). The ICV, downhole permanent pressure gauges, and the downhole flowmeter provide complete surveillance and control of the injection operation to achieve reservoir management and optimize the waterflood objectives.A world-class Pliocene oil reservoir in the deepwater GOM underwent significant pressure depletion due to a weak water-drive mechanism. Extensive subsurface studies and modeling suggested great rock quality and reservoir connectivity, favorable oil-water mobility ratios, and significant upside potential making this reservoir a perfect candidate for waterflooding. Given topsides facility space constraints, a topsides injection was ruled out. Seawater injection via subsea pumping was deemed risky and marginally economical given the high cost and low commodity prices. The asset team then brainstormed ways to minimize the cost and overcome the associated risks and challenges. The asset team envisioned a dumpflood scenario would overcome all the challenges, but a dumpflood had not previously been implemented in the deepwater GOM. From a technical standpoint, all the known risks were identified and addressed, and a low risk factor was determined for this project.After a complex well completion job, the injection rate was ramped-up to ≥20,000 bwpd water via the ICV. An immediate uptick in reservoir pressure and production rate was observed in the producer well 3,000 ft away. Continuous injection has resulted in reservoir pressure and flowrate increases by at least 1,000 psi and 4,000 bopd, respectively, consistent with reservoir modeling estimates.The operator was successful in implementing an existing technology in a unique way in the deepwater environment. A naturally occurring water source at a depth of 19,000 ft was efficiently harvested to increase recovery from a reservoir at a fraction of the cost of a conventional deepwater waterflood project. Great interdisciplinary collaboration and forward thinking enabled the success of this unique project, opening up tremendous possibilities to increase recovery from other fields where a conventional waterflood may not be justifiable.

A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.

Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources. A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.