Work Flow Uses Vaca Muerta Data To Optimize Asset Evaluation
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Asset evaluation embraces the integrated analysis of a hydrocarbon-bearing field, and the identification of suitable strategies for its future development, to add incremental value for the investor(s). Optimizing the evaluation process under uncertainty is important particularly in unconventional reservoirs, which hold large quantities of oil and gas resources but also exhibit large degrees of uncertainty. This paper describes a comprehensive optimization-under-uncertainty work flow that combines a simulation-based approach with semiautomatic work flows and high-speed computers to facilitate the process of decision-making for investors, using data from the Vaca Muerta Formation in Argentina as an example.
An asset evaluation depends on many input parameters, some of which are partially known, partly analyzed, or unavailable. Yet a go/no-go decision must be made, frequently within short time frames, because of competition, changing conditions, or the chance to take advantage of the business opportunity. The decision usually is based on preliminary assumptions and a conscientious analysis of several possible outcomes. Identifying the suitable future development strategies and the estimation of uncertainties in the input variables is crucial. Knowing the possible variability of the input and how the field mechanisms function will allow probabilistic forecasts of parameters such as production, costs, prices, and revenues. In the case of unconventional reservoirs with very limited history and high development costs, optimization under uncertainty plays a significant role in maximizing profit, reducing investment risk, and facilitating the decision-making process.
The authors summarize the optimization-under-uncertainty work flow that was implemented for this study. The starting point is the development of a base-case, single-well, matched simulation model, and, where available, an extended model with history-matched offset wells. This is followed by sensitivity analysis to identify the most-influential parameters; uncertainty analysis and proxy modeling for developing probabilistic forecasting profiles (type wells); and optimization of key parameters under existing uncertainty, which is the final objective of this paper. The model and the uncertainty and optimization work flows have been built in the Petrel platform and all the simulations have been executed in the Eclipse compositional reservoir simulator, using published Vaca Muerta data.
Base-Case Single-Gas-Well Model
A critical element of any single-well simulation study is developing a base-case simulation model that correctly captures all the fluid-flow mechanisms that take place during the life of the well, while running as fast as possible. The characteristics of the base-case single-well simulation model and the mechanisms that were considered are discussed briefly, because the main objective of this work was to focus on probabilistic forecasting and optimization.
Static and dynamic data are captured and integrated to construct the base-case model. These include formation tops, well deviation, petrophysical analyses, production data, geomechanical and perforation logs, stimulation data, volumetric production, wellhead and bottomhole flowing pressure, and representative pressure/volume/temperature and special core-analysis data. Fig. 1 shows an example of such an integration work flow.
Different methods can be used to simulate the production behavior of an unconventional well. For the sake of demonstrating the proposed work flow, a single-well, dual-porosity and compositional simulation model was constructed, assuming a gas reservoir.
Hydraulic-fracturing treatments in organic shales tend to generate complex fractures over a volume of rock. A schematic representation of the complex fractures branching out from the main hydraulic fracture for three contiguous transverse fractures is shown in the complete paper. Hydraulic-fracture complexity is enhanced (is less planar) as a result of lower stress anisotropy and a larger density of natural fractures (sealed or open) or planes of weakness. To model the stimulated complex fracture volume propagating from each perforation cluster, a rectangular volume is defined in the simulation model, considered the stimulated rock volume (SRV), and assigned properties of enhanced permeability.
Using this simulation method, three values should be defined for each parameter in the dual-porosity model: the SRV fracture cells, the non-SRV fracture cells, and the matrix cells whose properties are considered constant inside and outside the SRV. Two tables are defined for the pressure and saturation-dependent variables (such as relative permeabilities, and fracture and matrix degradation factors): one for the matrix cells, and another for the fracture cells.
History Matching and Prediction-Model Construction
During history matching, the wells are controlled by reservoir volumetric rate. The objective is to match the flowing bottomhole pressure of the entire wellstream and the flow rate of each phase. A table in the complete paper displays the reservoir parameters that should be adjusted during the history-matching process implementing the previously described model.
Despite the high vertical thickness of the Vaca Muerta shale, long, horizontal, multistage wells have proved to be the most-efficient and economical way to exploit the blocks. In this study, the same approach was followed. Therefore, all the results and discussion regard multistage, fractured, horizontal wells.
In the base-case prediction and the subsequent sensitivity and uncertainty analyses, the authors used a well spacing of 400 m, a lateral length of 2500 m, and 25 hydraulic fracture stages, with each stage containing four fracture clusters. However, because these values are assumptions based on the last trends in Vaca Muerta—and for the purpose of demonstrating the work flow only—readers are cautioned not to use them as guidelines for well or field planning.
For prediction scenarios, the wells were put under bottomhole-flowing-pressure (FBHP) control. An FBHP decline based on the typical historical BHP decline on offset wells should be calculated. The flowing-pressure decline rate is by itself a parameter for optimization that can be translated to controlled production for maximizing expected ultimate recovery (EUR).
The authors discuss determination of the uncertainty ranges and sensitivity analysis, stating that, before performing uncertainty analysis, it is important to determine the effect of different parameters on the EUR and peak production rate to help reduce the number of variables and, therefore, the overall simulation run time.
Uncertainty Analysis and Proxy Modeling
After eliminating the least-influential parameters on the EUR, eight parameters were selected as a short list to be incorporated in the final uncertainty analysis runs. The Monte Carlo sampler using the Latin Hypercube method was chosen as the sampling method. In the complete paper, several figures illustrate the simulated gas production rate and EUR results, the range of variables used for the simulation runs, and the results of the uncertainty runs and the range of the outputs for EUR, gas initially in place, and recovery factor per well. Another figure shows the probabilistic curves generated using the tuned proxy model for the uncertainty runs. An example is presented of a comparison of simulation results with actual data for a 12-month cumulative measurement.
During the optimization process, the total study area was restricted to 10×10 km. Considering 2.5-km length per well and 0.4-km well spacing, 100 wells could technically be drilled within the area. All costs were estimated on the basis of published data and rounded up or down for the sake of simplicity.
By the time of this study, the natural gas price in Argentina was subsidized to stimulate local industry and replace gas imports, which could justify the extra cost of increasing the number of wells and clusters per well. Using the unit development cost (UDC)—the ratio of the total capital expenditure of the project divided by the total volume produced in barrel oil equivalent—as the objective function, the capital cost of the wells and facilities was calculated. For an optimal case, the UDC must be minimized.
After defining the objective function, a procedure to integrate uncertainty in the optimization was followed.
- For this example, with eight variables using a Monte Carlo sampler with the Latin Hypercube method, 80 runs were made to demonstrate the work-flow applicability.
- For any combination of well spacing and cluster spacing, the same combination of variables was run to calculate the objective function.
- The mean value of objective functions for any set of runs was calculated and the run set which gave the minimum value of the objective function was selected as the optimum case.
The optimized case was a run set that gave a UDC equating to $8.76/BOE. On the basis of the calculations, the additional recovery from increasing the number of wells and perforation clusters could not justify the extra capital expense.
- The work flow proved to be efficient in execution time, repeatability, and reliability for the period for which the production data exist. It demonstrated an ability to replace empirical methods and capture all the physics, which is taken into account during the optimization process.
- The base case serves to capture all possible production mechanisms and, therefore, history match various kinds of wells within their production period.
- Characterization of influential variables is of paramount importance. These include uncertainty distribution type [probability density function (PDF)], boundary range or truncation of PDF, and correlations. The ranges of uncertainty for variables greatly affect the optimized scenario.
- Comparing simulated results with the performance of analog wells is necessary to ensure that correct uncertainty ranges are captured.
- Optimization under uncertainty can add value for the decision-making process, increasing the chances of higher project profitability by unlocking hidden potential that otherwise could be overlooked.
- Other elements were not investigated, but could also be included as optimization variables (e.g., pace of development, scheduling of drilling and completion activities, number of rigs, facilities constraints, and pumping volume per cluster).
- For the case example, Run Set 4 (500-m well spacing and 40-m cluster spacing) provided the highest value of objective function.
- Decreasing fracture density and porosity increases the optimal number of stages and the cost of the well.
Work Flow Uses Vaca Muerta Data To Optimize Asset Evaluation
01 July 2019
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09 July 2019