Canadian Natural Resources Ltd. used two new drilling optimization techniques developed by Baker Hughes—an expert system for drill bit selection and software to predict the achievable rate of penetration—to improve risk management in Western Canada.
In a time when the industry is trying to cut costs and improve drilling performance, more companies strive to understand better their exposed project risks and rewards based on their capital investment. To ensure the balance of risk and reward is maintained, it is essential to understand where improvements can be made and to what extent they may affect the overall drilling operation.
Drilling optimization practices and techniques can be used to improve risk management by improving equipment selection and performance prediction, helping operators assess the amount and impact of investment required for a given drilling project.
Many service companies develop specific enabling technologies to achieve this.
This article describes the development and use of two such technologies in drilling optimization—an expert system for drill bit selection and software to predict the achievable rate of penetration (ROP) that were used for a Canadian Natural Resources Ltd. (CNRL) project in Western Canada.
The expert system is a rule-based bit selection system that uses a detailed description of the drilling environment, including meter-based lithology, synthetic wireline logs, predicted pore pressures, and anticipated operating parameters of the well or hole interval being analyzed to produce a bit selection recommendation including IADC bit type and features.
The ROP algorithm has been developed as a drilling optimization tool and attempts to model the technical limit ROP that can be expected through a given hole interval. The ROP algorithm uses as its inputs: detailed lithological descriptions of the anticipated formations, hole size, mud weight, predicted pore pressure, bit type, and anticipated operating parameters, from which it calculates an accurate, meter-based ROP prediction.
The ROP algorithm improves drilling decisions and provides performance analysis, while guiding financial planning. The algorithm can be used in the planning phase of a project to develop time curves based on expected performance and to compare and contrast potential bit and bottomhole assembly (BHA) pairings based on performance predictions. Furthermore, the ROP algorithm can be used in post-well analysis to identify areas where potential drilling performance was not achieved and help in identifying improvements for future projects.
Both systems have been employed in several drilling environments worldwide and comparisons with actual drilling performance have been used to modify the calculations and improve predictions.
Introduction; system development
An expert system for drill bit selection1 2 has been in development for more than 10 years. This development has used knowledge extraction and engineering techniques to encode bit design and application knowledge from experts in the developer's various research and application departments.
The process resulted in a highly complex set of rules which model expert understanding governing the selection of drill bit features according to the physical properties of the drilling environment under study.
Rule bases have been developed which separately deal with impregnated, PDC, steel tooth, and tungsten carbide insert (TCI) bits. Each rule base represents generically the major component features of the drill bit (cutting structure, bearing type, seal type, gauge enhancements, etc.) and an understanding of the effect of a range of rock and environmental properties on their selection. Such environmental factors represented include, but are not limited to, unconfined compressive strength, interfacial severity, bit run length, BHA type, etc.
Statistical analyses of the rock properties within an application are included in the derivation of other attributes (e.g., abrasivity, hardness, meterage) that are accumulated over the entire bit run length. This analytical approach allows the system to make decisions on bit selection and section drillability in both homogeneous and heterogeneous drilling applications.
Supplementary rule bases exist which represent expert knowledge of drillability problems in rule format. In essence, once the system is loaded with the physical description of the drilling environment, it is able to generate a suite of bit and feature recommendations for each bit class, provide an identification of potential drillability problems, and suggest recommended operating practices to mitigate these problems.
Each rule base is continually updated with new research and validated against field applications. The benefits of utilizing an expert system approach like this to support engineering decision making processes are that the system:
- Always considers all known bit selection criteria in order to give a viable bit choice recommendation for a given interval.
- Provides an improved basis for developing "what-if" or contingency scenarios in the well planning or if drilling progress differs from what is planned.
- Improves the consistency of the recommendations made through time, by different personnel, across geographic boundaries.
- Serves as a decision support tool and training aid for less experienced staff.
- Is used in the planning, drilling, and post-well analysis phases of a drilling optimization project to qualify bit selection and operating practice recommendation.
Drilling optimization
The expert system provides decision support to engineers in all phases of the analytical process used in drilling performance optimization.
In the planning phase, the system builds a depth-matched offset well description using a combination of manual inputs and imported offset well data.
During the implementation phase, a new well description can be compiled from a combination of the actual drilling data and the offset well description which can then be further iterated in the event of unplanned geological or bit run changes.
In the postwell analysis phase, a complete description of the actual well can be modeled and the review of recommendations based on this new description can be performed to iterate the bit selection for subsequent wells and to validate the rule base for other applications.
The well description in the expert system consists of discrete information for the well trajectory, fluid type, BHA type, and formation tops. The depth matched meter-based data (from offsets) is imported in ASCII format into the system. This imported data set consists of:
- Interpreted lithology.
- Compressional and-or shear sonic travel time.
- Gamma ray.
- Proposed bit rpm.
- Proposed weight on bit.
- Mud weight.
- Pore pressure.
- Bit run length.
- From this data set the expert system derives:
- Unconfined compressive strength.
- Bit run hardness.
- Abrasivity.
- Bit run abrasivity (discrete).
- Estimated achievable (EROP).
- Bit run mean EROP.
These main attribute derivations (meter-based data), together with other intermediate attributes and the discrete well description are then passed to the expert systems' inference engine. The engine compares the attribute values with the rules defined in the system to generate the bit feature, problem identification, and operating practice recommendations (Fig. 1).
ROP estimation
Many factors influence the instantaneous rate of penetration (ROP), including rock properties, borehole and pore pressures, mud properties, bit design and wear state, operating parameters and bit hydraulics. Further, the ROP can be reduced by the occurrence of many different drillability problems such as bit or drill string vibrations, bit balling, etc.
There has been much work in the past to build models capable of predicting rate of penetration. In general these have suffered from one of two limitations.
Mechanistic models usually require access to input parameters that are not normally known or readily measurable. Conversely, empirical models require calibration against ROP measurements made in the environment in question.
These cannot readily make allowance for drilling problems that occurred when those measurements were made, thus have limited ability to predict penetration rate if those problems were to be controlled.
An ROP model has been developed from the concept of mechanical specific energy (MSE). This is the energy required to excavate a unit volume of rock.
The model involves three steps.
First, the minimum specific energy that can reasonably be expected at the depth in question MSEmin is estimated from wireline log data, lithology, and downhole pressures by use of an empirical relationship developed from laboratory drilling test data.
Next the power, W, transmitted by the bit into rock destruction is calculated from the weight on bit, rotary speed, bit diameter, and a friction factor that depends on bit type and rock properties.
Finally, the instantaneous ROP is estimated from the minimum specific energy, the hole diameter, Dia, and the power input to rock destruction: ROP = 2,538 * W / (MSEmin * Dia 2) with ROP (fph), "W" (hp), MSEmin (1,000 psi), and Dia (in.).
The resulting estimated instantaneous rate of penetration can be thought of as that which would be seen if the most appropriate bit for that interval of rock had been selected and if no significant drillability problems were to occur.
This model has been validated with both laboratory and field data and been shown capable of making reasonable ROP predictions in a wide variety of drilling environments (Fig. 2). It can be used in several ways in drilling optimization projects. During planning, it can help set drilling time targets and expectations. Additionally, comparing the model's predictions with penetration rates seen in offset wells can reveal areas of suboptimal performance with potential for improvement. And during drilling, comparison of current penetration rates with those predicted by the model can assist the detection of drillability problems.
Case study-Western Canada
CNRL used the expert system and ROP algorithm in planning a 4,550-m vertical gas well in the foothills of Western Canada. Offset wells in the area had typically been difficult to drill, usually requiring 150-180 days to reach total depth (TD), and 30-55 bit runs in the highly variable lithology. The formations have typical unconfined compressive strength (UCS) values ranging from about 5,000 to 35,000 psi throughout the well.
On past wells, bit selection had typically been based on past experience of local personnel, through trial and error or personal preferences, without attempting to optimize bit selection by hole interval or rock properties.
In the case study, a detailed analysis of the anticipated rock properties, pore pressure, planned BHAs, and operating parameters was conducted to identify projected bit run intervals. These data sets were then input to the expert system to develop a recommended bit program for each hole interval that identified the IADC bit type, and bit features specific to tricone and PDC bits. Fig. 1 shows an example of the bit recommendations for this case.
Once the bit recommendations for the target well were developed through the expert system, this information was added to the collective data sets and used in the ROP algorithm to calculate the technical limit ROP for the entire well.
Because this was the first time that the ROP algorithm was applied to a well in Western Canada, it was not proposed as an ROP target but rather as a test case to compare the predicted vs. actual ROP and evaluate the validity of the prediction and potential for use on future wells.
Comparison of the predicted to actual ROP shows that where the predicted drilling parameters matched the actual drilling parameters, there was generally close agreement, and throughout the well the general trend of the actual ROP performance matched predicted performance. Results showed there was generally better agreement through shale and siltstone formations than in sandstone formations, indicating there may have been some error in the pore pressure analysis.
During the planning phase, engineers observed that a highly detailed, good-quality lithology estimation was required to obtain a realistic ROP prediction.
The expert system's drill-bit recommendations were used as a guideline to evaluate various bit proposals to determine the best and most cost-effective bit program for the well. The expert system's recommendations were more aggressive than the drill bit vendors recommended programs through several intervals, due to the use of application-specific bit features rather than being based solely on past bit programs.
External influences prevented the recommendations from being always followed throughout the entire well. When these recommendations were followed, however, CNRL experienced significant increases in both run length and ROP over offset wells.
The closest offset well required 37 bits to reach a TD of 4,496 m (measured depth). The target well was drilled to a TD of 4,552 m (MD) with 26 bits with bit-life increases of up 33% with TCI bits achieved. In addition to the reduced bit consumption, CNRL achieved ROP improvements of 15%, 52%, and 60% in the 311.2 mm, 215.9 mm, and 142.9 mm sections, respectively.
It should also be noted that while the use of the expert system was an important tool in the overall optimization of the drilling process on this well, a component of the improved performance increase was associated with improved BHA design, operating procedures, and drilling practices. Any attempt to separate these would be subjective at best.
The well's progress curve shows that the optimization process, including the use of the expert system, saved about 15 drilling days from the planned time curve. At an average spread cost of $50,000/day, this translates into a cost savings of $750,000.
Learning
The expert system takes into account the effects of many different variables and assesses not only their interaction but provides an estimate of drilling performance. Often engineers will optimize one component of an operation without considering how this component will be affected by the hundreds of other components required to drill a best-in-class or pacesetter well.
We have validated our rule base in a number of geographically dispersed and physically diverse drilling applications. Recommendation quality in these data sets matches closely the recommendations of experienced local drill- bit engineers, although the system does not use any localized geographical expertise.
In roughly 80% of bit runs, the system recommends the same cutting structure as the local expert, and in the remaining cases the recommendation is heavier set (more conservative), and within two cutting structure codes of that of the expert.
The expert system and ROP algorithm will be utilized on future optimization projects and the refinement of modeling process is underway to resolve misleading or bad log data with respect to lithology determination and comparisons with striplog data. The process described has provided encouraging results to date and the value will be further quantified on future projects.
Acknowledgment
The authors thank Canadian Natural Resources Ltd. and Baker Hughes for their support of this project and their permission to publish this paper.
References
1. Fear, M.J., Meany, N.C., and Evans, J.M., "An Expert System for Drill Bit Selection," SPE/IADC Drilling Conference, Dallas, Feb. 15-18, 1994.
2. Evans, J.M., Fear, M.J., and Mean, N.C., "A New Graphical Representation for Rule Definition and Explanation in an Expert System," proceedings of Expert Systems '95, 15th Annual Tech. Conf. of the British Computer Society Specialist Group on Expert Systems, Cambridge, December 1995.
The authors
David Curry (david.curry@ bakerhughes.com) is director of research at Hughes Christensen, The Woodlands, Tex. He previously served as Baker Hughes OASIS technical manager. Curry has more than 20 years' industry experience as an R&D manager and scientist. He holds an MA in natural sciences (1973) and a PhD in fracture mechanics (1976), both from Fitzwilliam College, Cambridge. Curry is a Fellow of the Institution of Mechanical Engineers, a member of the Institute of Materials, Mining, and Minerals and the SPE, and he is a chartered engineer.
Phil Perry ([email protected]) is knowledge systems manager at Baker Hughes OASIS, Aberdeen. He has also served as an optimization engineer at Baker Hughes and as data manager at British Gas. Perry has 15 years of industry experience and has co-authored nine industry papers and technical articles. He holds an MS in sedimentology (1993) and a BS (1989), both from Reading University, UK. Perry is a member of the SPE and the Petroleum Engineering Society of Great Britain.
