SummaryThe impact of brine salinity and ion composition on oil recovery has been an area of research in recent years. Evidence from laboratory studies, supported by some field tests targeting mainly sandstones, has distinctly shown that injecting low-salinity water has a significant impact on oil recovery. Although the potential for carbonates has not been thoroughly investigated, some reported studies have excluded carbonates from this effect. The main objective of this paper is to investigate the potential of increased oil recovery by altering the salinity and ionic composition of the injection water for carbonate reservoirs, define the recovery mechanisms, and eventually transform the emerged trend to full-fledged reservoir technology.This paper presents the results of different laboratory studies to investigate the impact of salinity and ionic composition on oil/brine/rock interactions and draws conclusions on potential recovery mechanisms. Also, it provides a laboratory coreflooding study conducted using composite rock samples from a carbonate reservoir to investigate the impact of salinity and ionic composition on oil recovery. The experimental parameters and procedures were well designed to reflect the reservoir conditions and current field injection practices, including reservoir pressure, reservoir temperature, and salinity and ionic content of initial formation water and current types of injected water.The experimental results revealed that substantial tertiary oil recovery beyond conventional waterflooding can be achieved by altering the salinity and ionic content of field injected water. The new emerged trend is distinct from what has been addressed in previous reported studies on topics of low-salinity waterflooding for sandstones or seawater injection into high-temperature chalk reservoirs. On the subject of recovery mechanisms, the results showed that altering the salinity and ionic composition of the injected water has a significant impact on the wettability of the rock surface. Also, nuclear-magnetic-resonance (NMR) measurements indicated that dilution of seawater can cause a significant alteration in the surface relaxation of the carbonate rock and also can enhance connectivity among pore systems because of rock dissolution. The results, observations, and interpretations addressed in this study provided compelling evidence to suggest that the key mechanism for the emerged trend is wettability alteration.

The impact of brine salinity and ion composition on oil recovery has been an area of research in recent years. Evidence from laboratory studies supported by some field tests targeting mainly sandstones, has distinctly shown that injecting low salinity water has a significant impact on oil recovery. Although, the potential for carbonates has not been thoroughly investigated, some reported studies have excluded carbonates from this effect. The main objective of this paper is to investigate the potential of increased oil recovery by altering the salinity and ion composition of the injection water for carbonate reservoirs, define the recovery mechanisms, and eventually transform the emerged trend to full-fledged reservoir technology. This paper presents the results of a laboratory coreflooding study conducted using composite rock samples from a carbonate reservoir to investigate the impact of salinity and ionic composition on oil/brine/rock interactions, and eventually on oil recovery. The experimental parameters and procedures were well designed to reflect the reservoir conditions and current field injection practices, including reservoir pressure, reservoir temperature, salinity and ionic content of initial formation water and current types of injected water. Also, this study provides detailed discussion and interpretation for potential mechanisms.The experimental results revealed that substantial tertiary oil recovery beyond conventional waterflooding can be achieved by altering the salinity and ionic content of field injected water. The new emerged trend is distinct from what has been addressed in previous reported studies on topics of low salinity waterflooding for sandstones, or seawater injection into high temperature chalk reservoirs. On the subject of recovery mechanisms, the results showed that altering the salinity and ionic composition of the injected water has a significant impact on the wettability of the rock surface. This was also confirmed by nuclear magnetic resonance (NMR) measurements. The results, observations, and interpretations addressed in this study provided compelling evidence to suggest that the key mechanism for the emerged trend is wettability alteration.

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The objective of this paper is to present the application of a new approach to identify a preliminary well test interpretation model from derivative plot data. Our approach is based on artificial neural networks technology.The paper illustrates the application of this new approach with a field example. The mathematical derivation and implementation of this approach can be found in Ref. 1.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWell-test model identification and, subsequently, model parameters determination are more complex in horizontal wells than in vertical wells. This is due to the increase in number of flow regimes occurring during a flow period and the fact that strong correlation exists between model parameters.This study presents a new approach for automatic model identification and computer-aided well-test interpretation in horizontal wells. The new approach is based on using neural network to (1) identify the well-test interpretation model; (2) identify flow regimes; and (3) mark the position of identified flow regions on the derivative plot of well test data.This work consists of first generating common model signatures using Ozkan and Ragavan analytical solutions for horizontal wells in various reservoir and inner boundary conditions assuming laterally boundless reservoirs. Next, these signatures are used to train neural networks for three identification stages; model identification, flow regime identification, and position of flow regime identification. Separate networks were trained, then tested and validated using synthetic as well as field data. Once the three identification stages are completed, specialized plots for data points falling into each flow regime are used to determine initial model parameters. Final model parameters are determined using nonlinear regression.A comparative study was carried out using different network architectures. Modular approach with direct data utilization is found to be most suitable for implementation of our approach.