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.
Al-Rudaini, Ali (Heriot-Watt University) | Geiger, Sebastian (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Maier, Christine (Heriot-Watt University) | Pola, Jackson (Heriot-Watt University)
We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected.
Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator.
Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low.
The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.
The Alvheim field, offshore Norway, has subsea wells with long horizontal branches completed with sand screens. After 10 years of production, water production starts to constrain the oil production. Mechanical water shut-off is impossible in these wells, hence other methods are of interest. In a well workover in 2013, two high-viscous polymer pills were bull-headed and squeezed into the reservoir. The well productivity was reduced with around 50% while the water-cut dropped and pointed to potentially 3 mmstb of extra oil recovery. A research study was initiated with the objectives to understand the changed well performance and if polymer bull-heading can be a future method to reduce water production and enhance oil production.
An experimental laboratory program started with filtration tests of polymer solutions based on the polymer used in the well operation. Core flood experiments were performed by injecting polymer into two parallel mounted cores, then back producing these individually with either water or oil. Several combinations of parallel cores were tested with polymer injection: high vs. low permeability, high oil saturation vs. low oil saturation, outcrop sandstone vs. Alvheim core, as well as two different polymer versions.
The polymer recipe as used in the well operation showed to plug standard filters with filter size larger than the reservoir pore sizes but did not plug the cores. The polymer recipe as used in the well gave a better disproportionate permeability reduction (DPR) than the alternative polymer variant with similar viscosity. A theoretical model for the shear rate in the porous media matched the experimental measured data excellent. The core results show a stable permeability reduction factor of 100-450 for water, while only a factor 2-10 and decreasing with time for oil. The achieved DPR ratio of 45-80 is better than the trend from earlier published results.
The DPR as measured in the laboratory was next integrated in the reservoir model as part of the history match of the treated well. The Alvheim field has several reservoir zones separated with thin shales, and this reservoir zonation seems key for this EOR method to work.
The laboratory work, the reservoir studies and the field experience all point to a possible robust and simple EOR method for Alvheim and similar oil fields. The polymer seems to act as a "magic filter", allowing oil to pass while not water. Future work includes more research and maturing a new polymer pilot on Alvheim.
Gaither Draw Unit is a heterogeneous and tight formation with an average permeability less than 0.1 mD. After more than 1.7 MMSTB water injection, there was no clear indication or benefit of the injected water from any producer. However, knowing the distribution of the injected water is critical for future well planning and quantifying the efficiency of injection. The objective of this study is to show how the Capacitance-Resistance Model (CRM) was used on this field and validated using other independent methods.
The CRM model describes the connectivity and the degree of fluid storage quantitatively between injectors and producers from production and injection rates. Rooted in material balance, signals from injectors to producers can be captured in the CRM. Using constrained nonlinear multivariable optimization techniques, the connectivity is estimated in the selected portion of the field through signal analysis on injection and production rates. In this tight formation, the whole field is divided into seven regions with one injection well and surrounding producers to conduct CRM analysis. We further use integrated but independent approaches to validate the results from CRM. The validation includes full field modeling and history match and fluid level measurement using echometering technology.
This paper focuses on a real field water flooding project in Gaither Draw Units(GDU). CRM is used to detect reservoir heterogeneity through quantifying communication between injectors and producers, and attains a production match. The fitting results of connectivity through CRM indicate permeability regional heterogeneity, which is consistent with full field modelling. The history matched full field model presents the saturation distribution showing that the majority of injected water mainly saturates the surrounding regions of injectors, and the low transmissibility slows down the pressure dissipation. Overall, the comprehensive interpretation obtained through these three independent methods is consistent, and is very useful in planning infill well drilling and future development plan for the Gaither Draw Units.
This paper shows that it is critical to integrate different sources of data in reservoir management through a field case study. The experience and observations from this asset can be applied to other tight formations being developed with water flooding projects.
Special Core Analysis, SCAL data has a direct impact on the way fluids are allocated and distributed in the reservoir simulation models, which would directly impact reservoirs’ STOIIP estimation and their distribution. Moreover, it directly affects the performance of secondary and EOR flooding processes, and in turn impacts the accuracy of the oil and gas reserve estimates, and the management of these reserves. Therefore, SCAL data could be considered as one of the most critical reservoir input data for reservoir simulation models. This course will shed light on the theoretical and experimental background of SCAL data. It will explain the concept of reservoir wettability and different factors that could induce changes in reservoir wettability.
Spontaneous and forced imbibition are recognized as important recovery mechanisms in naturally fractured reservoirs because the capillary force controls the movement of the fluid between the matrix and the fracture. For unconventional reservoirs, imbibition is also important because the capillary pressure is more dominant in these tighter formations, and a theoretical understanding of the flow mechanism for the imbibition process will benefit the understanding of important multiphase-flow phenomena such as waterblocking. In this paper, a new semianalytic method is presented to examine the interaction between spontaneous and forced imbibition and to quantitatively represent the transient imbibition process. The methodology solves the partial-differential equation (PDE) of unsteady-state immiscible, incompressible flow with arbitrary saturation-dependent functions using the normalized water flux concept, which is identical to the fractional-flow terminology used in the traditional Buckley-Leverett analysis. The result gives a universal inherent relationship between time, normalized water flux, saturation profile, and the ratio between cocurrent and total flux. The current analysis also develops a novel stability envelope outside of which the flow becomes unstable caused by strong capillary forces, and the characteristic dimensionless parameter shown in the envelope is derived from the intrinsic properties of the rock and fluid system, and it can describe the relative magnitude of capillary and viscous forces at the continuum scale. This dimensionless parameter is consistently applicable in both capillary-dominated and viscous-dominated flow conditions.
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.
Electromagnetic (EM) images of oil and water in the ground are likely to be seen on the computer screens of a lot more engineers as a result of a collaboration between Halliburton and GroundMetrics. The small San Diego company will gain the international marketing presence of the giant service company, which will be working with it to better integrate EM with other downhole data to better model and simulate what is going on in a reservoir. The companies are imaging different aspects of a reservoir, potentially offering a more complete view of how the fluids and the rock interact. Halliburton gathers and integrates an array of seismic and logging data that define the shape and makeup of the formation. GroundMetrics' equipment uses EM energy to measure where fluids are concentrated, and can tell oil, water, and gas apart because they respond differently.
Water-alternating-gas (WAG) injection has demonstrated encouraging results for improving oil recovery. However, numerical simulation of three-phase flow and the associated hysteresis effects are not well-understood. In the complete paper, a new assessment of the WAG-hysteresis model, which was developed originally for water-wet conditions, was carried out by automatic history matching of two coreflood experiments in water-wet and mixed-wet conditions. The results indicate that history matching the entire WAG experiment would lead to a significantly improved simulation outcome. Using an optimization software, the authors have carried out a series of history-matching exercises on coreflood experiments to evaluate the performance of the WAG-hysteresis model and to simulate WAG experiments conducted on mixed-wet and water-wet cores at near-miscible conditions.
High capillary pressure has a significant effect on the phase behavior of fluid mixtures. The capillary pressure is high in unconventional reservoirs because of the small pores in the rock, so understanding the effect of capillary pressure on phase behavior is necessary for reliable modeling of unconventional shale-gas and tight-oil reservoirs. As the main finding of this paper, first we show that the tangent-plane-distance method cannot be used to determine phase stability and present a rigorous thermodynamic analysis of the problem of phase stability with capillary pressure. Second, we demonstrate that there is a maximum capillary pressure (Pcmax) where calculation of capillary equilibrium using bulk-phase thermodynamics is possible and derive the necessary equations to obtain this maximum capillary pressure. We also briefly discuss the implementation of the capillary equilibrium in a general-purpose compositional reservoir simulator. Two simulation case studies for synthetic gas condensate reservoirs were performed to illustrate the influence of capillary pressure on production behavior for the fluids studied.