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Though there are various methods to assess reservoir performance, historical methods seem to focus the assessment on a single or couple of parameters. These include traditional methods to evaluate the reservoir sweep through average oil saturation-thickness maps, remaining oil volume maps, etc. Optimum reservoir management is a challenging and time consuming process since it usually involves analyzing many reservoir properties such as porosity, permeability, thickness, hydrocarbon saturation, fluid properties, relative permeability, net-to-gross ratio and pressures. In this work, we incorporate all these parameters into an automated workflow for reservoir diagnostics; and identification and ranking of optimum hydrocarbon (HC) targets. The proposed workflow extracts static and dynamic information from reservoir simulation outputs and performs additional post-processing calculations on each grid cell for all time steps. The methodology involves classification of the reservoir simulation grid cells based on fluid saturation, relative permeability, pressure changes and displacing phase fluxes. After that, Produced, Mobile and Immobile oil volumes are calculated for each cell. These volumes are then grouped into six categories, namely, Produced, Highly Contacted, Moderately Contacted, Minimally Contacted, Uncontacted and Immobile Oil. In addition, the workflow incorporates different indicators for determining grid cell quality. These indicators are Reservoir Opportunity Index (ROI) and Simulation Opportunity Index (SOI); and we proposed a new reservoir quality indicator that incorporate changes in pressure over time. Finally, the workflow identifies connected cells with high quality indices and ranks these regions based on size and/or grid cell quality as potential targets for infill drilling. The presented automated workflow is introduced as an integral part of well placement optimization workflow. It has been tested on several simulation models and successfully identified and ranked un-swept reservoir regions which proved through dynamic simulations to be credible future drilling targets.

There are several types of porosity-permeability transforms to determine permeability from well log-derived porosity in uncored oil and gas wells. Widely used typical transforms, either by geologic zones or by facies, do not provide satisfactory permeability to be distributed in reservoir simulation models. Hydraulic Unit (HU), which is a technique of rock typing of reservoir, provides far superior transforms than typical transforms. In this work, available routine core data, from major carbonate oil reservoirs located in eight different oil fields of the Kingdom of Saudi Arabia, are assembled to develop improved generalized porosity-permeability transforms by Hydraulic Units (HU). Improved transforms by HUs were developed for major carbonate oil reservoirs in eight different oil fields. Correlation accuracy (R2) of each transform by HU was very good (>0.9). Then all data from eight different fields were combined to develop generalized transforms by HU technique. This technique provided excellent transforms for various HUs regardless of rock fabrics—either limestone or dolomite. Developed generalized transforms by HUs were tested for their accuracies on acquired core data in two wells, completed in the same major carbonate reservoir, but in two different oil fields. Applicability of generalized transforms was also tested with the core data from a different secondary carbonate reservoir. Calculated permeability by HUs matched very well with the tested core data which enhanced the reliability of developed generalized transforms. Similarly, generalized vertical permeability transforms by HUs were developed for the same major carbonate reservoirs in eight fields. Calculated vertical permeability by HUs matched very well with core data as well. Then vertical to horizontal permeability ratios (kV/kH) by HUs were calculated using both generalized transforms for vertical and horizontal permeability. The kV/kH ratios for most of the HUs are found to be almost one under the laboratory condition. Introduction There are several types of porosity-permeability transforms to determine permeability, from well log-derived porosity, for wells which do not have core data. Geoscientists and engineers proposed several techniques to achieve better porosity-permeability transforms based upon routine core analyses data from a very limited number of cored wells. Most geological characterizations were rock oriented by emphasizing on depositional environments, lithology and facies. To describe fluid flow in porous media, pore system dependent models were also proposed. Several published methods are briefly discussed as follows: Transforms by Facies Figure 1 shows the traditional porosity-permeability crossplot resulted from routine core analyses data of 25 cored wells in a Saudi Arabian oil field. Permeabilities of Facies 1 to 4 are represented by blue, green, red, and black color data points, respectively. The permeability for the same porosity, for a facie, spreads with a wide margin that could span three orders of magnitude. Evidently, traditional porosity-permeability transforms for every facies as shown would not provide representative permeability distribution in the geological and reservoir simulation model. The incorrect permeability distribution could lead us in poor history matching, and consequently, the developed simulation model could not be utilized to predict future reservoir performance and business planning.

There are several types of porosity-permeability transforms to determine permeability from well log-derived porosity in uncored oil and gas wells. Widely used typical transforms, either by geologic zones or by facies, do not provide satisfactory permeability to be distributed in reservoir simulation models. Hydraulic Unit (HU), which is a technique of rock typing of reservoir, provides far superior transforms than typical transforms. In this work, available routine core data, from major carbonate oil reservoirs located in eight different oil fields of the Kingdom of Saudi Arabia, are assembled to develop improved generalized porosity-permeability transforms by Hydraulic Units (HU). Improved transforms by HUs were developed for major carbonate oil reservoirs in eight different oil fields. Correlation accuracy (R2) of each transform by HU was very good (>0.9). Then all data from eight different fields were combined to develop generalized transforms by HU technique. This technique provided excellent transforms for various HUs regardless of rock fabrics—either limestone or dolomite. Developed generalized transforms by HUs were tested for their accuracies on acquired core data in two wells, completed in the same major carbonate reservoir, but in two different oil fields. Applicability of generalized transforms was also tested with the core data from a different secondary carbonate reservoir. Calculated permeability by HUs matched very well with the tested core data which enhanced the reliability of developed generalized transforms. Similarly, generalized vertical permeability transforms by HUs were developed for the same major carbonate reservoirs in eight fields. Calculated vertical permeability by HUs matched very well with core data as well. Then vertical to horizontal permeability ratios (kV/kH) by HUs were calculated using both generalized transforms for vertical and horizontal permeability. The kV/kH ratios for most of the HUs are found to be almost one under the laboratory condition. Introduction There are several types of porosity-permeability transforms to determine permeability, from well log-derived porosity, for wells which do not have core data. Geoscientists and engineers proposed several techniques to achieve better porosity-permeability transforms based upon routine core analyses data from a very limited number of cored wells. Most geological characterizations were rock oriented by emphasizing on depositional environments, lithology and facies. To describe fluid flow in porous media, pore system dependent models were also proposed.