Ahmed Daoud

Production Allocation in Multi-Layers Gas Producing Wells Using Temperature Measurements With the Application of a Genetic Algorithm

A methodology has been presented of allocating gas rate and associated water to each individual layer using temperature measurements and total surface production of gas and water. In part 1, an analytical forward model has been proposed for wellbore temperature response under two-phase production in a multilayer geometry, using a nodal representation of the well. This model accounts for the formation geothermal gradient, steady-state gas-water flow in the wellbore, friction loss, and Joule-Thomson effect in the wellbore; in contrast to the thermal and physical properties of gas and water, wellbore heat losses due to unsteady heat conduction in the earth, and the mixing of the fluid streams of contrasting temperature. The second part shows the solution technique used to allocate the gas and water rate for each layer. The practical implementation of the new developed production allocation model is examined on data from two actual gas wells with temperature measurements taken from production logging tools recorded in these wells. The results show that the model succeeded to accurately allocate the flow rates of gas and water.

A Bayesian Integration of Volumetric and Material Balance Analyses to Quantify Uncertainty in Original Hydrocarbons in Place

Abstract The authors use a Bayesian formulation to integrate volumetric and material balance analyses. Specifically, they apply Bayess rule to the Havlena and Odeh material balance equation to estimate original oil in place, N, and relative gas-cap size, m, for a gas-cap drive oil reservoir. The authors consider uncertainty and correlation in the volumetric estimates of N and m, as well as uncertainty in the pressure data. They then quantify uncertainty in the estimates of N and m resulting from the combined volumetric and material balance analyses. By combining the analyses, each reduces the uncertainty of the other, resulting in more accurate in-place estimates than from the separate analyses.

Fast and Efficient Sensitivity Calculation Using Adjoint Method for 3 Phase Field-Scale History Matching

Adjoint method-based sensitivity for field-scale history matching with large number of parameters suffers from several limitations. First, the CPU time depends on the data points which are large for any brown fields of long history; second, it requires large memory to save the gridblock pressure and saturation per each time step used in the forward model. Third, it is computationally expensive as it requires solving the Adjoint system of equations backward in time per each forward time step which is usually of high magnitude in case of field scale applications of long history. Lastly, the solver used for solving the Adjoint system of equations needs to be efficient for large-scale applications. We propose an efficient and fast approach for sensitivity calculation based on the Adjoint method to overcome much of the current limitations. First, we use a commercial finite difference simulator, ECLIPSE, as a forward model, which is general and can account for complex physical behavior that dominates most field applications. Second, the production data misfit is represented by a single generalized travel time misfit per well, thus effectively reducing the number of data points into one per well. Third, we solve the Adjoint system of equations backward in time in larger time step that is equivalent to the time of severe changes in pressure and saturation due to the changing of well conditions or introducing new infill wells rather than using the forward model time steps. This approach reduces the computational effort and memory allocated for the sensitivity calculation. Fourth, we use an iterative sparse matrix solver, LSQR, for solving the Adjoint system of equations which shows high stability for field-scale applications. We demonstrate the power and utility of our approach using synthetic and pseudo field examples. The synthetic examples show the robustness and efficiency of our sensitivity calculation approach compared to the perturbation. The pseudo-field example has 10 years of production history with original gas cap and oil-water contact with strong aquifer support. Using well log data, core data, water-cut and gas-oil ratio history from producing wells; we characterize the permeability at each cell, thus demonstrating the feasibility of our approach for field applications. Introduction Conditioning geological models to production data is an important step in reservoir modeling to build a reliable model to be used in predicting the reservoir performance and in proposing the optimum field development plan. Conditioning the geological or the static model to production data is typically known as “History Matching” which is considered the most time consuming phase in building a reliable model for the field. Thus, any reduction in the time taken for this phase is very important to speed up the modeling process as the majority of the development plans should be based on examining it on the model before accepting it for practical application. Accordingly, building reservoir model for each field becomes a commonly used practice in the industry and any improvement in speeding up the reservoir modeling process is highly demanded. Different techniques are proposed to speed up the history matching process where all are grouped under what is called computer assisted or automatic history matching. The automatic history matching procedure involves the following steps; First, the forward model formulation, second, the data misfit calculation, third, the sensitivity coefficient calculation, and finally an optimization algorithm. First, the forward model used is the commonly used finite difference simulation, ECLIPSE, which is general, robust, and can tackle different physical problems.