As many fields around the world are reaching maturity, the need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more urgent. One of the more challenging and important problems along these lines is the well placement optimization problem. In this problem, there are many variables to consider: geological variables like reservoir architecture, permeability and porosity distributions, and fluid contacts; production variables, such as well placement, well number, well type, and production rate; and economic variables like fluid prices and drilling costs. Furthermore, availability of complex well types, such as multilateral wells (MLWs) and maximum reservoir contact (MRC) wells, aggravate this challenge. All these variables, together with reservoir geological uncertainty, make the determination of an optimum development plan for a given field difficult. The objective of this work was to employ an optimization technique that can efficiently address the aforementioned challenges. Based on the success and versatility of Genetic Algorithms (GAs) in problems of high complexity with high dimensionality and nonlinearity, it is used here as the main optimization engine. Both binary GA (bGA) and continuous GA (cGA) were tested in the optimization of well location and design in terms of well type, number of laterals, and well and lateral trajectories in a channelized synthetic model. Both GA variants showed significant improvement over initial solutions but comparisons between the two types showed that the cGA was more robust for the problem under consideration. The cGA was, thereafter, applied to a real field located in the Middle East to investigate its robustness in optimizing well location and design in more complex reservoir models. The model is an upscaled version for an offshore carbonate reservoir, which is mildly heterogeneous with low and high permeability areas scattered over the field. After choosing the optimization technique to achieve our objective, considerable work was performed to study the sensitivity of the different algorithm parameters on converged solutions. Then, multiple optimization runs were performed to obtain a sound development plan for this field. An attempt was made to quantify how solutions were affected by some of the assumptions and preconditioning steps taken during optimization. Finally, an optimization ran was performed on the fine model using optimized solutions from the coarse model. Results showed that the optimum well configuration for the reservoir model at hand can contain five or more laterals; which shows potential for drilling MRC wells. Other studies comparing results from the fine and coarse reservoir models revealed that the best solutions are different between the two models. In general, solutions from different runs had different well designs due to the stochastic nature of the algorithm but some guidance about preferred well locations could be obtained through this process

Current advances in the well completion technology have allowed for more complex smart well instrumentations with marginal additional cost. As an example, optical fibers can be run along horizontal wells to provide acoustic and temperature data that are distributed both in time and space. With such data at our disposal, an immediate evaluation of the well response is possible as changes in the reservoir or well occur. Most current work in distributed measurements looks at Distributed Acoustic Sensing (DAS) or Distributed Temperature Sensing (DTS) data individually, which limits our inferences about the multiphase flow problem. The objective of this work was to look at the two pieces of data together and determine what improvements can be achieved in multiphase flow problems compared to the conventional methods of looking at each DAS and DTS alone. The study began by evaluating the performance of DAS in analyzing two-phase flow; a process which begins by extracting the speed of sound within the fluid medium from the acoustic signal, then obtaining the phase fraction combination that obtains this speed of sound reading. Another procedure is explained to obtain similar results from DTS measurements. In this case, however, the in-situ phase fractions are correlated to the Joule-Thomson effect as reservoir fluids enters the wellbore. As both these procedures are limited to one- and two-phase flow applications, we extended the solution to work in three-phase flow problems by combining information from DAS and DTS. The flow profiling procedure was applied to two smart wells in the Middle East. Flowrates from different segments of the well were calculated and results were in close agreement with a surface flowmeter for most sections of the well. In cases where both DAS and DTS were not available for the same well, a commercial compositional and thermal reservoir simulator was used to generate synthetic examples. By applying the developed procedure, we found that cointerpretation of DAS and DTS data yields accurate in-situ three-phase fractions for all ranges of water cuts and gas volume fraction. In comparison, analyzing DAS or DTS individually is usually not sufficient to fully determine a three-phase flow problem.

Fiber optic distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) are considerably important for many applications. It is challenging to design a hybrid DAS-DTS system using the same optical fiber because the operation principles of the two sensors are different. We here deploy the widespread standard multimode fiber (MMF) for simultaneous distributed acoustic and temperature sensing. In particular, we operate the MMF in a quasi-single-mode (QSM) state to simultaneously fulfill the functionality requirements of the DAS and DTS. The reported design offers simultaneous vibration and temperature monitoring with high accuracy. In particular, the DAS has a 12.98 dB mean signal-to-noise ratio (SNR) and the DTS offers temperature measurements with ±1 °C accuracy. This technique is significant for many industrial applications because it efficiently tackles a longstanding issue in practical implementation.

As many fields around the world are reaching maturity, several drilling and completion technologies have been developed to boost production from these fields. Smart completions and distributed measurements are two of the most important tools in this category. This study examined a new smart completion tool, Distributed Acoustic Sensor (DAS) in terms of its utility for inflow monitoring and optimization in smart wells.We began by evaluating the performance of DAS as a tool for measuring downhole multiphase flowrate. A description of the methodology to calculate individual phase flowrates from acoustic signals, caused by pressure fluctuations in the flow, is presented. As DAS provides continuous flow of data from the wellbore, another opportunity emerges to optimize the flow from these smart wells. A methodology to use calculated downhole flowrates to update existing simulation models and perform, near real-time optimization is suggested for wells installed with DAS. As opposed to conventional optimization methods that rely only on reservoir simulation models, this procedure also makes use of real-time flow measurements. The methodology was tested on a synthetic model with encouraging results, where the optimum solutions obtained were in close agreement to the true optimum.The flow profiling procedure was applied to several actual wells. The first well was a single-phase oil producer. This process yielded speed of sound results that matched the fluid properties obtained in the lab. Flowrates from different segments of the well were calculated and results were in close agreement with a surface flowmeter for most sections of the well. More examples were conducted in two-phase flow wells with results being more qualitative and less conclusive. When the optimization procedure was applied for synthetic cases that have wells with similar completions to the tested ones, results showed that significant value could be realized by incorporating real-time measurements in the optimization process.Several advantages could be realized with the application of these methods. First, continuous downhole flow monitoring provides asset managers with more accurate allocation of their wells. Second, more accurate modeling for wellbore flow is possible by using in-situ phase flows to calibrate existing models. With more accurate models, evaluating different flow scenarios is possible before applying them on the field. Finally, quick decisions to change the controls of the well are easier with the described optimization method. By comparison, full reservoir simulation model optimization takes a very long time to make their use practical in everyday applications.