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PRODUCTION OPTIMIZATION AND PERFORMANCE BENCHMARKING TECHNIQUES FOR NEW INFILL WELLS USING IPR/VLP

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PRODUCTION OPTIMIZATION AND PERFORMANCE BENCHMARKING TECHNIQUES FOR NEW INFILL WELLS USING IPR/VLP

 

 

ABSTRACT

This Master thesis develops a new solution method for optimizing oil production at the Troll C platform. The Troll West field where Troll C is located is an oil rim field. The characteristic gas cap above the thin oil layer makes the Gas-Oil-ratio strongly rate de- pendent during production.  Today  the gas handling capacity is the main constraint as   the processing trains at the platform can process only a certain amount of gas. The pro- duction planning problem is today solved by applying state of the art software. However this software can not simultaneously solve all eight clusters at Troll C. Norsk Hydro ASA uses production data and experience to decide on an amount of gas that each cluster    may produce, before optimizing each cluster separately.

 

To improve this current practice the authors considered the use of linearization techniques and Lagrangian Decomposition as a new methodological solution platform. The idea has been to develop a solution method that split the total amount of gas capacity in a more optimal way. This is done by relaxing the gas handling capacity constraint into the objective function and use a Lagrange multiplier to penalize the use of gas. The authors have developed a Mixed Integer Linear Programming (MILP) model, and based on this built an algorithm implemented in VBA for Excel, Xpress-IVE and GAP. The algorithm has the possibility to dynamically increase the resolution of the linearized model close    to the area where the solution is believed to be. This formulation is completely scalable and can solve problem with any resolution, by accepting longer solution time. The solution method is based on Linear Problem (LP) methods,  SOS2,  Branch & Bound   and Lagrangian decomposition. The algorithm is tested on a test case to evaluate the performance of the algorithm. The information obtained was evaluated and taken into consideration before testing it on actual data from two out of the nine clusters at Troll C. This data was provided by Norsk Hydro ASA. The solution was compared with today’s solution, and produces 7,7% less oil.

 

Nomenclature

DInA           –   Dynamic Interpolation Algorithm

GAP            –   General Allocation Package

GOR           –   Gas/Oil-Ratio

GORM        –   Gas/Oil-Ratio Model

IC                –   Interpolation  Coordinate

ILP              –   In  ow  Perfomance Ratio

LaDA          –   Lagrangian Decomposition Algortihm

LD               –   Lagrangian Decomposition

PDP            –   Production Data Portal

PI                –   Productivity Index

P-line          –   Production line

PPOP          –   Production Planning Optimization Problem

PROSPER   –   Production and Systems Performance analysis software

RBC        –   Real Base Case

RTO         –   Real Time Optimization

SLP          –   Sequential Linear Programming

SOS1       –   Special Ordered Sets of type 1

SOS2       –   Special Ordered Sets of type 2

SQP         –   Sequential Quadratic Programming

TBC         –   Test Base Case

T-line       –   Test line

TOA         –   Total Optimization Algorithm

TWGP     –   Troll West Gas Province

TWOP     –   Troll West Oil Province

VLP         –   Vertical Lift Performance

 

 

INTRODUCTION

Oil production planning optimization has been practiced for many years and di erent models have been developed to assist oil producers to more e ciently extract the oil from reservoirs. In collaboration with Norsk Hydro ASA this work have had focus on the operational production planning at the Troll C platform. The special properties of the Troll reservoir make the production planning di cult. The most critical production factor when producing at the Troll C is the gas capacity. The oil is located in thin zones with large gas caps above, and one have to take into consideration the strongly rate dependent Gas/Oil Ratio (GOR) due to the gas-handling capacity of the rst stage separator. Today Norsk Hydro ASA use sophisticated software and technology to optimize their production.

 

In the oil industry, optimization models and methods have been used extensively for pro- duction planning problems. Wang [53] provides a thorough overview of oil production planning optimization, where both linear and nonlinear optimization techniques for com- mon oil production planning problems are presented. The most common concepts and components of an oil production optimization problem are presented in an informative way by Bieker, Slupphaug and Johansen [5]. This article gives a good overview of the subject and includes short descriptions on elements such as; processing facilities, well model updating, reservoir model updating, production planning, reservoir planning and strategic planning. Methods for well routing, gas lift and gas/water injections optimiza- tion are also reviewed. For more Troll speci c optimization Hauge and Horn [21] can be recommended. Here the challenges of operating and maintaining the Troll subsea system are described.

 

Today’s practice at Troll C is to allocate a certain amount of the total gas handling capacity to each cluster manually. Production optimization is often trial and error and good communication between operators and production engineers onshore are vital for success. By studying production data and simulate production, this experienced personnel decides on how much gas each cluster is allowed to produce. When this decision has been taken sophisticated software suites are used to optimize oil production by consider- ing the gas handling constraint. However, these software suites are not able to handle the routing of wells between the di erent production lines which also is part of the problem. Norsk Hydro ASA therefore needs to use brute force to check every routing combination for each cluster.

 

This master thesis develops a new solution methodology for the production optimization at Troll C. It considers the possibilities of assigning the gas capacities more properly to each cluster by solving all clusters simultaneously. The developed model is also going to handle routing of wells. Before developing this solution method the authors have stud-  ied relevant literature about petroleum production and operational research. A Mixed Integer Linear Programming (MILP) has been formulated and solution methods for this model are proposed. The solution method has been implemented in Xpress-IVE and Visual Basic for Excel (VBA). All non-linearity are handled through piecewise lineariza- tion and the use of Special Ordered Sets of type 2.  The resolution on the linearization     is dynamic. The model is further decomposed by use of Lagrangean Decomposition to relax the gas-handling capacity, where the Subgradient Optimization method is used to update the Lagrange multiplier. Beasley [4] provides a good description on Lagrangian Decomposition, while Jackson and Grossmann [27] uses Lagrangian Decomposition on a production planning problem from the process industry with good results.

 

The time horizon considered is one week. This implies that the reservoir conditions are treated as constant in this problem formulation. Further the production from each well is described by a piecewise linearized well performance curve generated in the General Allocation Package (GAP) developed by Petroleum Experts. GAP has also been used to simulate di erent combinations of owrates for oil, gas and water to construct a piecewise linearized multi-dimensional pressure drop function dependent on these rates. As this Master thesis is a rst step into this kind of solution methodology no gas lift wells or gas lift risers are considered.

 

The most di cult parts when building a production planning models is the modeling of the in ow from the reservoir to the wells and the pressure drop in the pipeline network. These subjects have been treated by several others. Handley-Schachler, McKie and Quintero [19] uses the concept of Special Ordered Sets of type 2 (SOS2) to linearize well performance curves when solving production optimization problem by Sequential Linear Programming. Their work also uses multi-dimensional SOS2 to model the pressure drop in the pipeline system, though not explained thoroughly. Litvak, Clark, Fairchild, Fossum, Macdonald and Wood [35] uses ow correlations together with Equations of State to calculate the pressure gradients. Kosmidis, Perkins and Pistikopolous [33] presents a mixed integer non linear model (MINLP) for daily well scheduling in petroleum elds using analytic ow relations when modeling multiphase ow and pressure drop relations. This model considers all of the issues in our Master thesis and more, except for the gas-handling constraint.

 

The developed algorithm’s performance is tested by  solving some test cases generated   by the authors and on data from a real case. The real case data is provided by Norsk Hydro ASA. The data consist of GAP les containing all information about the two clusters, including Norsk Hydro’s best solution. This data makes it possible to generate well performance curves and pressure drop functions in GAP.

 

The results are presented and discussed, both with respect to the performance of the MILP model and the potential value when planning the production at  Troll  C.  The results show that the dynamic linearization technique works effectively at the test cases; however it could not be used during testing of real data due to a malfunction. The Lagrange multiplier adjustment method works very well for both cases. The results from the real case shows that gas capacity are divided differently than the manual solution    used today, however it produces a bit less oil than the current solution.

All in all the different parts of the developed algorithm works as intended, however the assumption made about the pressure drop modeling seems to be a bit coarse. Therefore this part of the MILP model needs further development and testing before this tool can be used as an optimization tool at Troll C.

As part of this Master thesis the authors have studied a wide variety of literature on petroleum production planning and optimization.

The remainder of the report is made up of two major parts; the rst part describes the problem and the current practice. It starts out with a general description of the different levels of oil production planning optimization problems in Chapter 2. Chapter 3 describes the operational production planning problem considered at Troll C. Chapter 4 closes the first major part by describing the different models used to solve these types of problems and present the system topology and the mathematical model for the production planning at Troll C.

 

 

PURPOSE OF THE STUDY

The purpose of this work is to develop an alternative solution method to optimized production

 

OBJECTIVE OF THE STUDY

The objective of production system analysis can be summarized as:

  • Predict the optimum flow rate for specific reservoir
  • Predict the pressure drop in vertical tubing and annulus, and horizontal
  • Predict the pressure drop through surface well head
  • Determine the well head pressure and bottom hole pressure for specific
  • Determine the proper size of tubing, flow line and surface well head

SCOPE OF THE STUDY

The productivity of the well depends on an efficient use of the compressional energy available in the reservoir allowing the reservoir fluids to flow toward the production separator. This techniques uses IPR or VLP.

DEFINITION OF VERTICAL LIFT PERFORMANCE

This is a plot of the pressure traverse in the vertical/inclined tubing of a production system. It is a plot of the pressure losses vs. production rate in the system. Multiphase flow correlations are used in calculating the VLP.

VERTICAL LIFT PRESSURE LOSSES

  1. Hydrostatic losses- Due to the density of the fluid column
  2. Frictional losses- Due to the viscous drag
  • Kinetic losses- Due to the expansion and contraction of the fluid and the change in the cross sectional area, which leads to acceleration and deceleration of the fluid

FACTORS AFFECTING THE VERTICAL LIFT PERFORMANCE

  1. Production Rate
  2. Well Depth – GOR/GLR
  • Tubing Diameter – WOR

 

 

 

 

 

 

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