Gravity measurements may allow the description of different characteristics of a reservoir (Fedi et al., 2018, Figure 2). From gravity measurements over a specific area, local structural traps, stratigraphic traps, or fluid movement in the reservoir can be inferred if sufficient density contrast exists between the geologic feature of interest and the surrounding rocks. The differences in gravitational field measurements are determined by rock mass and depth of sources/structures. The surface-gravity technique can be applied to any field, depending on reservoir thickness, size, depth, porosity, and density contrast between the fluids. Rock and fluid density contrasts determine the response. A gravity data survey is generally less expensive but has less resolving power than a seismic exploration survey. The University of Naples in Italy in collaboration with CPG-KFUPM have apply methods to analyze the gravity field at many scales (Fedi, 2002), providing maps of extremely rich and articulated structural lineaments at various scales and different depth without resorting to standard filter separations, but simply enhancing the effects at a given scale (which normally are possibly obliterated by those at other scales). Thus, an insightful representation of the structural features of a given area is obtained. This is critical for planning new wells and for simulating/forecasting the reservoir production

Data Integration

A critical component of the proposed reservoir characterization and monitoring is data integration to find the most realistic model (Strack et al., 1991; Hordt et al. 1992; Vafidis et al., 2014; Autio et al., 2016; Demirci et al., 2017; Massa et al., 2018; Passalacqua et al., 2018; Varfinezhad et al., 2020). This includes 3D modeling as the most crucial tool but also various inversion and joint inversion methods (Autio et al., 2016, Figure 3).

The integration of geophysical data is aimed at expanding the links between different types of observations and measurements with the source model, to reduce uncertainties and improve the decision process. The joint interpretation of different datasets can be realized through different approaches that can be summarized as follows:

• Co-rendered images of different model parameters, such as conductivity and density, can be used for a sort of ‘level-zero’ geophysical integration.
• Constrained inversion: in this approach, the most robust dataset available is commonly used for constraining or driving the inversion of data characterized by a higher level of uncertainty.
• A more sophisticated approach based on cooperative inversion works better in complicated geophysical settings and is characterized by an iterative and interactive nature.
• The obtained geophysical data can also be used for a joint interpretation through machine learning algorithms, to minimize the interpretation risk and of achieving an improved understanding of the complex dynamics occurring in the area.
• The most complex method of integrating different datasets is simultaneous joint inversion. In this case, a composite (or joint) cost function, including the misfit between observed and predicted values for two or more independent datasets (e.g., electromagnetic and gravity) is minimized.

 

Enhanced Oil Recovery and Flood-Front Monitoring

The same combination of methods can be applied for flood-front monitoring. Improving the recovery factor is a crucial challenge in almost every oil field. Today, only limited technology exists from inside the wellbore, making thus mapping water/oil distribution difficult and expensive. Different fluids were tested to optimize the EOR process (WAG and others) and improve the reservoir oil recoveries as part of the enhanced oil recovery (EOR) process. Based on the fluids used for EOR, the electromagnetic geophysical method is one of the best options for monitoring (based on the depth of investigation and to the sensitivity of the technique to fluid changes) and if it is combined with borehole seismic and microseismicity measurements (which predominantly responds to structural changes), then the maximum of the information from the data integration will be revealed.

Dynamic fluid-flood monitoring can easily improve the recover factor by 20-30% in addition to the reduction of operational and development costs. As mentioned above, the best choice of method for direct fluid movement is electromagnetics, as practiced in borehole logs and analyzed in the previous section. The EM, combined with gravity measurements, will allow the imaging of the fluids in the carbonates.

A monitoring project is usually divided into 3 phases, where each phase ends with a detailed plan for the next phase (Figure 4).

Phase 1: 3D Feasibility
Phase 2: Proof-of-concept (Can we see reservoir parameter variations?)
Phase 3: Field Pilot

Based on the different phases, ee expect to be able to map porosity/permeability variations in carbonate rock, integrate it with other geoscientific information, and increase the in-fill drilling success ratio significantly. This would yield a production rate by reducing the total cost at around 30-50%.