The purpose of history matching is to achieve geological realizations calibrated to the historical performance of the reservoir. For complex geological structures it is usually intractable to run tens of thousands of full reservoir simulation to trace the most probable geological model. Hence the inadequacy of the history-matching results frequently leads to poor estimation of the true model and high uncertainty in production forecasting. Reduced-order modeling procedures, which have been applied in many application areas including reservoir simulation, represent a promising means for constructing efficient surrogate models. Nonlinear dimensionality reduction techniques allow for encapsulating the high-resolution complex geological description of reservoir into a low-dimensional subspace, which significantly reduces number of unknowns and provides an efficient way to construct a proxy model based on the the reduced-dimension parameters.
Polynomial Chaos Expansions (PCE) is a powerful tool to quantify uncertainty in dynamical system when there is probabilistic uncertainty in the system parameters. In reservoir simulation it has been shown to be more accurate and efficient compared to traditional experimental design (ED). PCEs have a significant advantage over other response surfaces as the convergence to the true probability distribution is proved when the order of the PCE is increased. Accordingly PCE proxy can be used as the pseudo-simulator to represent the surface responses of the uncertain variables. When the objective and constraints of a reservoir model is described by multivariate polynomial functions, there are very efficient algorithms to compute the global solutions. We have developed a workflow at which incorporates PCE to find the global minimum of the misfit surface and assess the uncertainty associated with. The accuracy of the PCE proxy increases with the additional trial runs of the reservoir simulator.
We conduct a two dimensional synthetic case study of a fluvial channel as well as a real field example to demonstrate the effectiveness of this approach. Kernel Principal Component Analysis (KPCA) is used to parameterize the complex geological structure. The study has revealed useful reservoir information and delivered more reliable production forecasts.
PCE-based history match enhances the quality and efficiency of the estimation of the most probable geological model and improve the confidence interval of production forecasts.
Wu, JinYong (Schlumberger) | Banerjee, Raj (Schlumberger) | Bolanos, Nelson (Schlumberger) | Alvi, Amanullah (Schlumberger) | Tilke, Peter Gerhard (Schlumberger - Doll Research) | Jilani, Syed Zeeshan (Schlumberger Oilfield UK Plc) | Bogush, Alexander (Schlumberger)
Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators.
The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence.
This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no ‘one-approach-fits-all' when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.
Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, Voidage Replacement Ratio (VRR) and Net Present Value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field development activities on the environment while NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well placement optimization has been done mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of one (or close to one) during waterflooding.
A multiobjective approach incorporating NPV and VRR is proposed for solving the well placement optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase has the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights were used in the third phase to describe the relative importance of the NPV and the VRR and a comparison of how these weights affect the optimized NPV and VRR values is provided.
We applied the method to determine the optimum placement of wells using two sample reservoirs: one with a distributed permeability field and the other, a channel reservoir with four facies. Two evolutionary-type algorithms: the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE), were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary oil recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of one.
In this paper, a data-driven model is applied to derive optimum maintenance strategy for a petroleum pipeline. The model incorporates structured expert judgment (classical model) to calculate the frequency of failure, considering various failure mechanisms. Optimization models are applied to derive optimum maintenance intervals for petroleum pipelines on the basis of the frequency of failure estimated. Two separate maintenance-optimization models are proposed. The first is a use-based optimization model that minimizes the expected total cost from a petroleum pipeline. The second is a benefit/cost (B/C) -ratio model that seeks to maximize the benefit derived from the pipeline, while minimizing operation and failure costs. The B/C-ratio model is less data intensive, and it has been used to optimize failure data obtained in the classical model. In this approach, the maintenance optimization is a further attempt at reducing the influence of subjectivity in maintenance decisions.
Oil industries with higher potential reservoirs are having restrictions to maximize the oil production when the gas handling capacities are limited with considerations to environment. There can be other limitations such as water handling capacity and well-head sensitivity limitations. To overcome these limitations this paper provides a comprehensive method, which was developed with combination of mathematical tools like curve fitting techniques to build well model from production test results and linear/ nonlinear programming for optimizing the well models.
This method summarized can be applied to optimize/ maximize oil production in matured fields, fields with limitation on gas handling and high GOR limitations. Principle used in this paper can be used in fields with water handling limitation also. This will help reducing impact on environment as well.
Results and Conclusions
Results for maximum oil which can be produced with the gas plant design conditions, limitations are provided in this paper and set of well choke opening for the optimum production are generated by program for different cases. Previous works used the approach to optimize mainly gas lift wells, this paper proposes for oil production optimization, moreover previous works created well performance curves on the basis of oil production or estimations, but this work is based on choke opening and well test results of encoded wells.
A novel method of combining well test data to interpolate oil, gas, water production equation (Well performance curves) in terms of well choke opening (which is decision variable) is used. This paper provides optimum oil produced from field, Maximum oil which can be produced from field with different Gas limitations, and field maximum oil production capacity. All the above results are generated without using any specialized software.
Chronostratigraphic seismic interpretation provides insights into basinal evolution. By mapping the internal stratigraphic architecture, this method has been widely used for well placement optimization and reservoir model building.
Over many years of research at TOTAL, the Sismage team has created GeoTime™, a software tool for automatic chronostratigraphic analysis of seismic data, particularly in complex geological settings. This tool can be used to automatically or semi-automatically track the chronostratigraphic surfaces. The output is a 3D cube of stratigraphic surfaces (iso-geological time) which can be used to build the frame work for geomodels. A 3D cube of "Wheeler Diagram?? is also obtained by segmenting the seismic volume into different depositional or non-depositional packages. This diagram provides better understanding on the spatial distribution of deposition through the geological time.
In this paper, we will firstly introduce the GeoTime™ methodology, and then demonstrate two application examples. The first example is taken at a regional scale, where GeoTime™ was used to automatically track the major stratigraphic surfaces within a first order progradation system. The seismic volume was then segmented into sub order sequences bounded by the automatically tracked surfaces. 3D Wheeler diagram was also constructed to understand the depositional history. The second example is taken at reservoir scale from a producing field with more than 800 borehole penetration. In this example, we imposed geological constraint into the GeoTime™ workflow, and improved the previous interpretation of higher order prograding clinoforms for placing new development wells.
Lv, Songfeng (University of Electronic Science and Technology of China) | Zhang, Jiashu (University of Electronic Science and Technology of China) | Hu, Guangmin (University of Electronic Science and Technology of China) | He, Zhenhua (University of Electronic Science and Technology of China)