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Abstract Acquaintance of production changes is crucial to successful production forecasting of all reservoir types. However, the performance prediction of CBM is very complex due to its specific production behavior. In the case of the coalbed methane formations two-phase flow, which is caused mainly by hydraulic fracturing process occurs. Discussed production system is more complicated than single-phase flow and finding the relationship between produced gas and produced water is required. In this paper, we propose a novel mathematical model for two-phase flow phenomena in CBM reservoirs. The proposed model uses analogies to natural competition processes and is based on the Lotka-Volterra model, which is one of the most popular populations dynamics model. We implement set of two logistic equations with additional term for the purpose of gas and water production forecasting and modeling their declines curves. This set of two ordinary differential equations was originally used to model dynamics of populations, which compete to each other. Both interspecies and intraspecies interactions are considered in the proposed mathematical model. The developed model was validated by some simulated and field examples. The proposed model was firstly used to match the production history data (gas and water flow rates) in order to estimate the model parameters. These parameters were then used to predict the two-phase CBM production. The obtained results show a good match between the production data and created decline curves based on the proposed model. Therefore, the developed model can be used as a helpful tool in predicting the CBM two-phase production. Highlights • The novel mathematical model for two-phase production forecasting of CBM well has been proposed. • The proposed model uses analogies to natural competition processes and is based on the Lotka-Volterra model. • Decline curves for CBM gas and water flow rates were developed for some simulated and field examples. • Presented results show that the developed model has been successfully applied to CBM wells in the various production stages. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents novel optimisation model to evaluate the optimal number of wells for underground gas storage operations. More specifically, generalized well-placement scheme has been formulated to minimize the total number of wells required to fulfil prescribed multiple constrains including wells interference. The scheme integrates nonlinear models of reservoir and well performance. The optimization problem has been formulated mathematically and solved by iterative use of the SIMPLEX method. The method is dedicated to gas reservoirs or underground gas storages management. The method may be useful as a base for more detailed numerical reservoir simulation. The paper is illustrated by a field example related to optimisation of the production cycle of the underground gas storage in Poland. [Copyright &y& Elsevier]

Abstract One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time. Highlights • Novel self-adapt methodology to enhance well placement optimization was developed. • Significant reduction of optimization time was observed. • Artificial Kohonen neural network was implemented to solve complex engineering problem. [ABSTRACT FROM AUTHOR]

The application of Enhanced Oil Recovery (EOR) techniques are encouraged by the current oil price and the growing global demand for oil. Optimizing oil production from current resources becomes a main strategic target for many oil producing companies around the world. Among EOR processes, polymer flooding is an attractive option in many reservoirs. The objective of polymer flooding is to control water mobility inside oil reservoirs to ensure high oil recovery factors. Several designing parameters are critical for the success of polymer flooding applications. Chemical concentration depends on geological features of reservoir and well pattern are crucial to make decisions for the reservoir management. In addition, identifying the time of polymer injection is also important in a project as well as slug size. The goal of this article is to evaluate the efficient polymer injection strategy based on global optimization algorithms. Authors investigated the design of polymer flooding process in terms of optimal concentration, slug size and initiation time employ nature inspired algorithms coupling with reservoir simulation. Numerical testing and comparative study show that proposed natural-inspired algorithms outperform widely reported in literature optimization methods in that it provides a higher quality solution. [ABSTRACT FROM AUTHOR]

In order to achieve synergic environmental effect CO 2 -EOR can be applied to improve oil production and reduce carbon dioxide emission. Greater results can be obtained by the CO 2 huff-n-puff process which in the case of oil reservoirs is based mainly on the oil swelling effect. During the soaking period the primary mechanism distributing CO 2 in the reservoir is molecular diffusion. To calculate mass transferred to oil phase molecular diffusion coefficient is used. Its determination can be based on direct or indirect techniques but no standard method is normed. This paper presents a pulse-echo pressure-volume decay technique to determine oil-CO 2 diffusion coefficient under reservoir conditions with the use of acoustically monitored separator. In the proposed method measurements of difference between signal routing are recalculated to volume change in order to compute diffusion coefficient. Experimental procedure was conducted for three different temperatures. The obtained results are consistent with previous studies on diffusion coefficient determination. The proposed technique is more reliable and yields straightforward solution due to simultaneous binary measurements of pressure and volume system. Image 1 • A PEPVD technique to determine Oil-CO 2 diffusion coefficient with the use of acoustically monitored separator was develop. • The proposed technique yields straightforward solution due to simultaneous binary measurements of pressure and volume system. • Experimental procedure was conducted for three different temperatures. [ABSTRACT FROM AUTHOR]