Reservoir characterstics and profitability are two important constraints in any field development. Net present value (NPV) is usually used to measure the cash flow profitability profile and determine associated financial risks. On the other hand, voidage replacement ratio (VRR) is used to measure the rate of change in reservoir energy. In recent studies, VRR was used as an additional objective in well placement optimization. Such studies proposed minimizing single value of VRR that represents the entire reservoir. However, such single VRR value may not detect unbalanced distribution of reservoir pressure. Severe alteration of voidage replacement distribution could occur in the reservoir that could not be represented accurately by a single value of VRR. Therefore, we propose a more efficient method to overcome disparate regional pressure changes in well placement optimization. This new method is based on optimization of NPV constrained to regional average pressure of the reservoir. Differential evolution algorithm was applied to find the optimum well locations that yield the maximum NPV constrained to regional pressure balance. The regional pressure balance was achieved by specifying a maximum allowable difference between any two regional pressures. We evaluated four scenarios of reservoir development. The first two scenarios involve the use of predefined well patterns. In these scenarios, the well locations were selected without any optimization. The third scenario involves the optimization of well placement without constraining it to any pressure balance. The fourth scenario involves optimization of well placement constrained to regional pressure balance. The results obtained indicate that we can minimize the difference of average pressure between regions while achieving high values of NPV. This helps to have a uniformly distributed reservoir pressure throughout the production life of the reservoir.
Automatic updates of simulation models with historical field performance and events is a challenging and time-consuming task that reservoir engineers need to tackle; whether it is to maintain history matched reservoir models (evergreen assets), undertake new calibration exercise or update forecasting studies. The challenge takes another dimension with increasing complexity of field operations (production/injection/drilling/workover), and well designs and configuration of downhole equipment. This paper presents an efficient workflow capitalizing on IR4.0 Digital Twin principles to automate the process of seamlessly integrating and updating historical wells’ information in reservoir simulation models. The objective of this workflow is to drive reservoir simulation towards capitalizing on digital transformation and the Live Earth models concept to revolutionize model calibration and history matching for superior quality of prediction with great confidence. Well data digitization in this workflow was achieved through automating well data acquisition, well data quality checking enforcement and well modeling in interconnected simulation applications. The workflow minimizes human manual interaction with data giving engineers the chance to focus more on reservoir engineering aspects of reservoir engineering tasks. The workflow consists of four steps. The first step is data acquisition in which various types of well data are fetched. The second step is data quality check in which data from different data sources is subjected to engineering and scientific measures (i.e. Quality Indices) that translate engineering knowledge and experience to detect possible data inconsistencies. The third and fourth steps cover exporting and importing relevant data within the reservoir simulation applications’ portfolio where various data types are handled and managed seamlessly. Data and event acquisition workflows were automated to provide seamless well data transfer between different data sources and reservoir simulation pre and post-processing applications. The different types of well data were obtained through automatic fetching from data repository (databases, petrophysical models … etc.). The Quality Check (QC) procedures were automatically performed against deviation surveys, perforations, casing/tubing, flowmeter, cores, formation tops and productivity/injectivity index. This helped in identifying data discrepancy, if any, including missing data entries and contradicting well events. The automation of these workflows significantly reduced the time needed for well data transmission/update to the reservoir models, eliminated human errors associated with data entry or corrections, and helped keeping the models up-to-date (evergreen). Incorporating the digital twin concepts enabled advanced automatic digitization of well information. It provided a data exchange solution that meets E&P requirements and provided more effective and efficient methods of connecting diverse applications and data repositories.
Sweetspot identification methods are of significant value in optimizing well placement in reservoir simulation studies. These methods vary in their approaches due to the wide-ranging reservoir characteristics and different study objectives. This work analyzes a number of sweetspot identification methods and discusses their advantages and limitations. In addition, we establish a workflow that utilizes a combination of a number of reliable methods. A simulation model of a synthetic heterogeneous reservoir with six million grid-cells is used in this work to evaluate six sweetspot identification methods for the purpose of well placement. The evaluated methods use grid-cell productivity, fluxes and sweep ratio as well as a combination of a number of rock and fluid properties to generate sweetspot 3D maps. Using sweetspot maps from the analyzed methods and the proposed workflow, different well placement scenarios are developed and compared. Results are compared using total hydrocarbon production and voidage replacement ratio. We observe that wells placed using grid-cell productivity maps achieve significant improvement in the total hydrocarbon production over a period of ten years when compared to the other analyzed methods. This method identifies the high productive grid-cells which results in the best performance of wells among the analyzed methods. However, this method provided less emphasis on the grid-cells proximity and connectivity in the sweetspot map. In heterogeneous reservoirs, this can result in tortuous trajectory paths, which are impractical to drill. The flux-based method yielded less hydrocarbon production, but higher voidage replacement ratio. The proposed workflow demonstrated considerable improvements in the total oil production and a balance in voidage replacement ratio. The new workflow retained the advantages of different methods maintaining a balance between their strengths and marking distinct methodology that can be used for well placement optimization. This work highlights potential opportunities to improve the sweep efficiency in heterogeneous reservoirs by developing a hybrid workflow that integrates existing tools and methodologies.
As the oil and gas industry is continuously pushing boundaries of exploiting resources, it becomes more of a mandate to model and optimize forefront technologies. Multilateral wells are one example of a prevalent technology to maximize reservoir contact and return on investment. Optimum design and placement of this type of wells is significant. This work presents a multi-parametric optimization approach that optimizes the design of multilateral wells and maximizes the contact with highly productive hydrocarbon zones in the reservoir. Given a number of input parameters, the design and placement of multilateral wells is modeled using the Graph Theory principles and is optimized using Mixed Integer Programming (MIP) algorithms. The objective function is defined in this work as maximization function of the Total Contact with Sweetspots (TCS). At first, multiple main wellbores are optimized globally across the field and then several local optimizations are performed around each main wellbore to place the laterals. This optimization is subject to a number of input constraints, such as the maximum number of laterals, minimum spacing between wells, and maximum lateral length. Different sets of uncertainty parameters are generated using Latin-Hypercube Sampling (LHS) technique and used as input constraints in multiple well design realizations. In this work, the SPE10 benchmark model with 4 million grid cells and 10 existing producer wells was used. MIP was used in this work to optimize the initial geometry and placement of 20 new multilateral producers while LHS was used to fine-tune well configurations. Using TCS as the objective function in this multi-parametric optimization approach dramatically reduced the number of numerical simulation runs. The multi-parametric optimization generates multiple realizations with different sets of multilateral wells with different configurations. Numerical results from the benchmark model revealed the optimum solution with maximized hydrocarbon production. This resulted in a more practical approach to simultaneously optimize the placement of multilateral wells in large simulation models. In addition, the results reveal that the design, placement and performance of the new wells are highly sensitive to the sweetspot maps and reservoir heterogeneity. Using TCS as the objective function resulted in avoiding the excessive use of numerical simulation and cutting down the turnaround time for optimizing the design and placement of multilateral wells. In addition, the global and local optimizations used in this approach significantly simplified the mathematical formulation and avoided complex network modeling and optimization for multilateral wells.
Hydrocarbon wells are the most critical and challenging asset in any oil and gas field development and operation plan. The quality of the history matched reservoir simulation model and reliability of future field performance forecasts depend heavily on the accuracy of the well model. In typical reservoir simulation studies, a tremendous amount of time is devoted to gather and validate data required to construct the simulation model, particularly well related data, including well trajectories, completions, production and injection rates, well logs and downhole flow control devices. This issue can become more challenging when thousands of wells are involved with multiple configurations and complex completions. Therefore, it is critical to ensure the quality and accuracy of well data to have consistent, comprehensive and reliable reservoir models that can be used to forecast reservoir performance.In this paper, an advanced system for extracting, validating and pre-processing complex well information from the corporate database to perform well modeling and simulation is presented. The paper demonstrates how this system ensures the validity and accuracy of well models by applying advanced quality control measures with strong capabilities for detecting data inconsistencies. The paper starts with a description of the system and how the pre-processed wells contribute in building an integrated environment to serve complex well modeling. The paper demonstrates that the quality control process leads to an automated, efficient and easy well data processing procedure with a significant degree of reliability. In addition, real cases and lessons learned from this experience are discussed. The system implements new design and algorithms while dealing with a massive amount of data gathered from giant onshore and offshore oil and gas fields. This paper shows how Saudi Aramco applies creative solutions for the best utilization of the Upstream corporate data to support decision making, increase productivity and save costs.
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