Building static reservoir model that realistically captures facies heterogeneities relies on detailed interpretations of the sand body architectures from the integration of subsurface datasets and analogue examples. The delineation of stacked channelized turbidite sands was carried out, based on detailed petrophysical analysis, well log correlation and seismic interpretations. The continuity and connectivity of these sands remain a challenge despite having carried out detailed seismic attribute analysis for sand fairways mapping. For this study, we used a combination of well logs correlation and pressure transient analysis to resolve the sand connectivity and continuity challenges. Pressure transient analysis can be used to achieve both well productivity analysis and reservoir dynamic descriptive analysis. Interpretation of pressure transient well test can be done either by type curve matching or log-log derivative plot. A combination of log-log derivative plot and the Tiab Direct Synthesis (TDS) method were used to analyze the well test data. A log-log derivative plot is a plot of the pressure derivative against time with both axes on a logarithmic scale. Different geologic features such as faults, fractures and sand channels show unique fingerprint on the log-log derivative plots. The distance of the channel sand from the well was estimated using the Tiab Direct Synthesis method by making use of the gradient of the channel fingerprint on the log-log-derivative plot. The well test data from two wells at approximately 2.5km apart were plotted on the log-log derivative plot in other to identify the nature of the geologic features around the wells. In one of the well, channel sand signature or fingerprint was seen and the other well was not tested long enough to see the channel sand signature on the log-log derivative plot. The gradient from the channel sand signature on the log-log derivative plot was used in the TDS model to estimate the distance of the channel sand from the well. The TDS model estimated the distance of the channel sand from the well to be 51m. Based on the result of the pressure transient test analysis, we could establish that the sand is not continuously connected between the two wells. In combination with well logs correlation and seismic attribute analysis, the established sand fairways and baffles thus led to robust geological models with petrophysical properties that capture properly the lateral heterogeneity in the field.

The use of gas injection and storage approach in enhanced oil recovery (EOR) is receiving increasing attention as an efficient solution to mitigate the effects of anthropogenic greenhouse gas emissions in the atmosphere, improve production by means of increasing static reservoir pressure, and allow optimized utilization of produced hydrocarbons as a function of actual consumption. During gas injection a geological trap (i.e. active or abandoned reservoir) is used to store excess gas that can be eventually produced for future utilization. This process generates changes in pore pressure within the rock's porous space, affecting simultaneously the state of stress inside the reservoir and in its surroundings. These changes of the state of stress can be at the origin of instability mechanisms associated with fracturing inside and outside the reservoir and of reactivation of existing discontinuities (faults and fractures). If reactivation occurs within the caprock, this could lead to possible reservoir sealing failure and thus leakage of the stored gas at surface. Therefore, deformation of caprock and fault integrity must be assessed to properly manage containment performance and leakage-related risks. Given the intrinsic 3D nature of the problem, to ascertain the feasibility of injecting and/or re-injecting natural gas back into producing formations, it is essential to perform numerical simulations to capture the link between concomitant pressurization and /or depletion. The modelling is done with a 3D reservoir simulator and in-situ stresses changes are obtained by means of a 3D coupled reservoir geomechanics simulator. A feasibility study of gas injection and storage in a producing reservoir was performed using coupled geomechanics modeling within an E&P software platform. The process started from single-well geomechanics analysis and then passed through 3D structural characterization and properties modeling, in-situ preproduction stress modeling, dynamic simulation, and, finally, injection modeling. Analyses were carried out using injection pressure modeled dynamically in an industry-standard reservoir simulator. This allowed various injection scenarios to be explored, providing a 4D characterization at various time steps in the future of the state of stress within the reservoir and its surroundings. Results highlighted the main risks, which are related to loss of sealing for the caprock and reactivation of induced faults, as well as uncertainties associated with input parameters.

Arps in 1944 developed decline curve equations for analyzing reservoir/well production decline and the estimated decline rate constant is used for production forecast. In making reservoir production forecast, the Arps equations are usually calibrated to match the reservoir historical production decline by adjusting the decline rate constant value. The decline rate constant indirectly influences the reservoir properties responsible for production decline. However, the value assigned to the decline rate constant might either overestimate or underestimate the uncertainties in the reservoir rock and fluid properties responsible for reservoir production decline. The decline rate constant in the Arps equation has no equation relating it to the reservoir properties that are responsible for the reservoir production decline hence; the direction of uncertainty quantification on these reservoir parameters is not explicit, therefore undermining the predictive power of the Arps equations. The impact of reservoir properties uncertainties on the production rate decline and forecast by Arps equations cannot be examined due to the empirical nature of the Arps equations. Also, the decline rate constant is not explicitly related to reservoir rock and fluid properties responsible for production decline. However, the predictive power of the Arps model can be greatly improved if the decline constant can be expressed as a function of the reservoir rock and fluid properties responsible for production decline. Hence, the impact of uncertainty on the production forecast by Arps models can be examined. The objective of this work is to introduce reservoir rock and fluid properties into the Arps decline curve equations. This will enable the imposition of reservoir physics on the decline curve equations and the impact of uncertainties in these properties on matching historical production decline and forecast. In addition, a relationship between the decline rate constant in the Arps equations and the reservoir rock and fluid properties that influence reservoir production decline has been developed. A mechanistic approach using dimensional analysis was used to develop the relationship. The decline rate was assumed to be proportional to the various reservoir rock and fluid parameters, such as permeability, porosity, net-to-gross, drainage area, fluid density, and viscosity, that could influence production decline and by unit dimensional analysis, a relationship was developed between the decline constant and these reservoir rock and fluid properties. The decline rate constant was directly related to the product of reservoir permeability, fluid density, and the square of the pressure difference but inversely proportional to the cubic power of fluid viscosity. The relationship developed for the decline constant in this work will give the modified Arps model strong predictive power and allows for the impact of uncertainties on the production forecast to be evaluated. The results obtained from the modified Arps equations give better reservoir properties dependent outcomes than the traditional Arps equations and enable analysis of the effects of reservoir rock and fluid properties uncertainties on the production decline and forecast.