It is well known and documented that exploration of hydrocarbons is inherently a high-risk activity given a large number of uncertainties that may be present at any particular stage. The most common uncertainties are related to geological features such as structures, seals, and faults. On the other hand, economic analyses present uncertainties related to costs, probabilities, available technology and oil price. When coupled, these uncertainties can generate high-risk scenarios which may ultimately jeopardize the successful discoveries of assets.

In that light, the oil industry serves as a classic example of uncertainty in decision-making processes. Technological advancements have helped with breaking challenging barriers and developing well-established production strategies in a way that risk analysis and decisions have become vital components of this process. Demirmen [1] asserts that the types of development risks that need to be considered in a decision-making process are related to opportunity loss, uncommercial development and suboptimal development. The following figures exemplify the typical uncertainty in terms of economic evaluation (Net Present Value forecasting).

NPV Uncertainty

Although development risk is primarily a function of geological, economic and technology uncertainties, production strategy models can have a significant impact on the risk quantification. Especially in complex reservoirs, precise risk assessment requires high levels of detail that are only obtained through reservoir numerical simulation [3]. One possible difficulty while using numerical simulators is the necessity to set a production strategy, which is a function of the model; thus, production could also become an uncertain parameter.

Regarding reservoir characterization, the process of data acquisition begins as soon as a prospect is identified. As the first well is drilled, geologists and petrophysicists gather more data to create a static model. Each stage of this process carries a determined degree of uncertainty, especially due to wide data ranges. Uncertainty evaluation is a process which demands a considerable amount of effort, a variety of resources and extended knowledge. Thus, it is indispensable for the integrated team to carry out a suitable strategy for data collection and further analysis.

Uncertainty in reservoir models may stem from various sources and could be classified as follows:

  • Static model: outcrops and regional studies, well-log analysis, and seismic acquisition;
  • Upscaling: degree of coarsening for highly heterogeneous media;
  • Fluid flow modeling: PVT data, relative permeability curves, well productivity;
  • Production data integration: well-production data;
  • Production scheme and economic evaluation: optimization of the number of wells and their location, injection schemes, and surface facilities. [5]

Yet, according to Zabalza-Mezghani et. al. [5], there are three different statistical behaviors typically used to classify uncertainties:

  1. Deterministic: continuous parameters (porosity, permeability, matrix block size, etc.);
  2. Discrete: depositional scenarios, fault conductivity, aquifer size, etc.;
  3. Stochastic: equiprobable structure maps, fracture maps, history matched models, geostatistical realizations, etc

 

Schematic procedure for analyzing reservoir uncertainties [2]

During the first years of production, uncertainties play a crucial role as they make processes more complex and contribute to non-uniqueness in solutions. As production begins, a higher quantity of data promotes a better matching of information; meanwhile, complexity increases due to a greater amount of data regarding the reservoir development stages. In that light, Schiozer el. Al., [4] propose a different approach which integrates uncertainty analysis and history matching. Instead of starting a history matching procedure by running a base case, an uncertainty analysis is performed in a previous step. Therefore, potential models that do not reproduce the reservoir’s behavior are automatically discarded. Ultimately, this speeds up history matching and increases the reliability of the process as a whole.

Additionally, a series of studies that aim to develop methods to assess uncertainties in the oil industry have been conducted; these range from reservoir modeling to economic viability of a project. As an example, Khosravi et. al., [2] utilized a Design of Experiment (DOE) technique which is used to generate response surfaces and to identify critical factors that may be causing variations. In this case, Box-Behnken was chosen as the method of DOE in order to investigate oil recovery factor and pressure decline in fractured reservoirs. This could be explained by the method’s efficiency that essentially reduces the number of experiments, increasing the process speed.

In brief, as future hydrocarbons’ production tends to rely on difficult and challenging deposits, the pace of technological advancements will need to continually speed up in order to keep up with today’s production levels/needs. Furthermore, as the market faces a current crisis scenario, careful decision-making that minimizes risk is needed. Consequently, demand for sophisticated risk and decision analysis software will continue to increase. In this context, most of the methodologies described in the literature apply to geological and economic parameters. On the other hand, operations and reservoir characterization still present uncertainties of difficult manipulation, which result in a myriad of bottlenecks and constraints and demand further work.

 

REFERENCES

  1. Demirmen, F., 2001, “Subsurface Appraisal: the Road from Reservoir Uncertainty to Better Economics”, SPE 68603, SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, 2-3 April.
  2. Khosravi, M., Rostami, B., Fatemi, S. “Uncertainty Analysis of a Fractured Reservoir’s Performance: A Case Study. Oil & Gas Science Technology – Rev. IFP Energies nouvelles, Vol 67 (2012), No. 3, pp 423-433.
  3. Schiozer, D. J., Ligero, E. L., Santos, J. A. M. “Risk Assessment for Reservoir Development Under Uncertainty. Journal of the Brazilian Society of Mechanical Sciences and Engineering vol. 26 no. 2, Rio de Janeiro, April/June 2004.
  4. Schiozer, D.J., Almeida Neto, S.L., Ligero, E.L., Maschio, C. “Integration of history match and uncertainty analysis”. Journal of Canadian Petroleum Technology, Canada, v. 44, n.7, p. 41-47,  2005.
  5. Zabalza-Mezghani I., Manceau E., Feraille M., Jourdan A. “Uncertainty management: From geological scenarios to production scheme optimization”, J. Petrol. Sci. Eng. 44, 1-2, 11-25. 2004.