The Key Features of IPDA^TM

Advantage & Disadvantages of Top-Down, Full Field, SubSurface Modling

• Advantages
  • Much more intuitive and easy to follow than conventional simulation.
  • Can be performed by petroleum/reservoir engineers without specialized skills and training for simulation.
  • Analysis can start with minimal data (monthly production rates). Accuracy of the analysis increases as a function of more data being available.
  • Availability of additional data such as logs, cores, and well test etc., even for a subset of wells in the field, contribute to higher accuracy in modeling (not all well need to have the same and/or a complete set of data).
  • Ideal for mature fields with multiple wells and a healthy production history.
  • Arrives at reasonably accurate (qualitative) field-wide reservoir characteristics in relatively short period of time.
  • To be used as the sole modeling technique when conventional simulation and modeling is impractical due to data or budget constraints.
  • To be used as a complement to conventional simulation and modeling when such model already exists or is in the process of development.

• Disadvantages
  • Requires a minimum of 35 to 50 wells. More wells contribute to a better and more accurate reservoir model.
  • May not be used in new fields.
  • Provides qualitative rather than quantitative field-wide reservoir characteristics.

Proprietary & Unique Algorithms/Modules

• Fuzzy Pattern Recognition:
  Fuzzy Pattern Recognition (FPR) is a unique and proprietary algorithm that is capable of deducing understandable patterns and trends from complex and seemingly scattered data. Implementation of FPR technology in IPDA results in development of field-wide two and three dimensional maps from production indicators that have been generated from Decline Curve Analysis, Type Curve Matching, History Matching, Volumetric Reserve Estimation and Recovery Factor as well as statistics of production rate data.

• Integration of Sound Reservoir Engineering with AI&DM:
  Well known reservoir engineering techniques (Decline Curve Analysis, Type Curve Matching, History Matching, theory of Image Wells, Volumetric Reserve Estimation, etc) are integrated with state-of-the-art Artificial Intelligence and Data Mining - AI&DM - techniques (Artificial neural networks, Genetic Optimization, Fuzzy Set Theory, Voronoi Graph Theory, etc) in order to release the information content of the most available data in the oil and gas industry, namely monthly production rate data and well logs.

• Full Field Modeling and Analysis Based on Direct Measurements:
  Until now reservoir management through full field modeling of complex and mature reservoirs has been the domain of reservoir simulation. IPDA offers an alternative full field modeling that can substitute conventional simulation techniques in cases where reservoir simulation is cost prohibitive, and a complement to the existing efforts and analysis where reservoir simulation is or has been performed.

  Unlike reservoir simulation that is a bottom-up approach (where geological model is the foundation and it is integrated with fluid flow and fine-tuned through history matching) to full filed modeling, IPDA offers a top-down full field modeling (where geologic model is inferred based on integration of direct measurements in the field such as production, well logs and well tests via state-of-the-art AI&DM techniques).

Learning From Past Performance

• Decline Curve Analysis:
  Decline curve analysis can be performed on all the wells in the field. It will take an average of only a few seconds per well to perform Decline curve analysis using the intelligent techniques in IPDA. Results of Decline curve analysis are saved and used in the following steps.

• Type Curve Matching:
  Using the proper type curve for the given situation and making use of the information generated during the Decline curve analysis, several reservoir characteristics are identified during the type curve matching process. It will take an average of only a few seconds per well to perform type curve matching in IPDA. The approach used in IPDA makes the results of type curve matching repeatable by removing the inherent subjectivity that is associated with type curve matching process. Results of type curve matching analysis are saved and used in the following steps.

• History Matching:
  The information generate in the previous two steps, namely decline curve and type curve matching analyses are forwarded to a single-well radial numerical simulator as the initial conditions in order to history match the production data. It will take an average of only a few iterations to achieve a history match for each of the wells. Results of the history matching are saved and used in the following steps.

• Iterative Intelligent Integration, i³:
  The above three processes (Decline Curve Analysis, Type Curve Matching and History Matching) are iteratively integrated until all three processes agree on a set of reservoir characteristics for each well. These common characteristics will be the base of the rest of the analysis.

• Volumetric Reserve Estimation:
  Using Voronoi Graph Theory and the principles of image wells to identify the potential locations for formation of no-flow boundaries, an ultimate drainage area is assigned to each well in the field. Using data such as net pay, porosity, saturation and depth (when available from well logs) volumetric reserves is estimated for each of the wells and for the entire field.

• Recovery Factor:
  Cross referencing the results of Volumetric Reserve Estimates with Decline Curve Analysis a Recovery Factor is estimated for each well and for the entire field.

• Field-Wide Fuzzy Pattern Recognition:
  Using ISI's proprietary Fuzzy Pattern Recognition (FPR) algorithm, the information generated in the previous steps is mapped on the entire field. This powerful algorithm can decipher complex scattered information along the latitude and longitude and deduce understandable patterns in form of two and three dimensional maps that can be effectively used for field development strategies, identifying what needs to be done in order to maximize production and recovery from the field.

Making Decisions for Future Developments

• Identification of Sweet-Spots as a Function of Time:
  Using the FPR algorithm different production indicators and reservoir characteristics can be mapped on the field in order to identify the Relative Reservoir Quality Indices (RRQI). Sweet-spots in the field can then be identified by comparing and contrasting the developed maps. By performing the analysis based on time dependent production indicators (One, three, five … year cumulative productions) movement of sweet-spots in the field as a function of time (that is an indication of depletion) can be identified.

• Estimation of Remaining Reserves as a Function of Time:
  Using the FPR algorithm in conjunction with Decline Curve Analysis Estimated Remaining Reserves can be identified as a function of time.

• Optimization of Infill Locations:
  Cross referencing the maps generated for sweet-spots and remaining reserves with reservoir characteristics such as permeability (results of type curve matching analysis) one can identify the optimum infill locations in the field. Using the powerful modeling tools available in IPDA along with the economic analysis module one can estimate the potential performance of the infill wells and calculate their economic impact.

• Recovery Factor Enhancement:
  By recalculating the new drainage areas as a result of new infill wells, user can quickly calculate the enhancement in the recovery factor that is achieved as a result of drilling and producing the new infill wells. Using this technique, one can optimize the locations of the infill wells in order to achieve maximum recovery enhancement.

• Identification of Under-performer Wells:
  At the conclusion of IPDA analysis a list of under-performer wells are presented. These are wells that have the potential to produce more than they have been producing and are recommended for treatments such as workover and/or stimulation.