This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150079, "Managing Fields Using Intelligent Surveillance, Production Optimization, and Collaboration," by Frans G. Van den Berg, SPE, Andrew Mabian, SPE, Ronald Knoppe, Edwin Van Donkelaar, Frans Terlaan, and Valentin Koldunov, SPE, Shell, and Rufina Lameda, Science Applications International Corporation, prepared for the 2012 SPE Intelligent Energy International, Utrecht, The Netherlands, 27-29 March. The paper has not been peer reviewed.
From the point-of-view of a solutions provider the wastewater treatment should be straight forward: once given the composition of the feed and the required composition of the effluent, today's technology allows formulating a set of solutions which best meets the operator's and the regulatory criteria.
The problem with wastewater in the unconventional gas exploration and production operations is that there are large volumes to be handled and treated. To add complexity, composition varies for the same well in time and varies even more from area to area of development. Also, the requirements for the cleaned fluid vary from operator to operator and by region. Moreover, management of the water based fluids is under the pressure and scrutiny of various regulating agencies: public, privately, or governmentally run. All these constraints make the vetting of treatment methods and technologies to be a very dynamic and intensive process.
Our findings during the process of formulating a set of solutions shows that a deep understanding of the problems, combined with close collaboration with the operators and regulators along with solid basic engineering practices are the key to success.
Our experience would benefit the new developments in other unconventional exploration and production area in Asia by showing the steps that were undertaken to insure solutions are up to the highest standards.
The process of finding and testing various waste water treatment technologies to formulate a flexible comprehensive set of methods will be described. Laboratory results of various samples of water will be presented as well as the challenges that were overcome for obtaining consistent, reliable analytical data. The oilfield tough requirement presented to new technologies translates as: rugged, flexible, mobile, and low cost.
Water is a precious commodity that is needed in all human activity and for life in general. The Oil & Gas industry uses and generates large quantities of this commodity (Produced Water Volume Report). On average, for every barrel of oil produced there are eight barrels of associated wastewater. Increasing the efficiency of water usage and improving its management is both a high priority among E&P companies and a subject of intense scrutiny for the communities in which they operate.
Water Necessity in Developing Areas
The availability of suitable water for hydraulic fracturing and the means for environmentally responsible water recycling and disposal are critical for sustainable unconventional development. Produced water that comes to the surface during oil and gas recovery presents a challenge for Marcellus drillers because of the scarcity of injection wells in the Appalachian region. Other areas, like West Texas (Permian Basin or Eagle Ford Shale) do not lack for disposal options but do suffer due to the arid climate and depletion of ground water resources.
An efficient two-stage algebraic multiscale solver (TAMS) that converges to the fine-scale solution is described. The first (global) stage is a multiscale solution obtained algebraically for the given fine-scale problem. In the second stage, a local preconditioner, such as the Block ILU (BILU) or the Additive Schwarz (AS) method, is used. Spectral analysis shows that the multiscale solution step captures the low-frequency parts of the error spectrum quite well, while the local preconditioner represents the high-frequency components accurately. Combining the two stages in an iterative scheme results in efficient treatment of all the error components associated with the fine-scale problem. TAMS is shown to converge to the reference fine-scale solution. Moreover, the eigenvalues of the TAMS iteration matrix show significant clustering, which is favorable for Krylov-based methods. Accurate solution of the nonlinear saturation equations (i.e., transport problem) requires having locally conservative velocity fields. TAMS guarantees local mass conservation by concluding the iterations with a multiscale finite-volume step. We demonstrate the performance of TAMS using several test cases with strong permeability heterogeneity and large-grid aspect ratios. Different choices in the TAMS algorithm are investigated, including the Galerkin and finite-volume restriction operators, as well as the BILU and AS preconditioners for the second stage. TAMS for the elliptic flow problem is comparable to state-of-the-art algebraic multigrid methods, which are in wide use. Moreover, the computational time of TAMS grows nearly linearly with problem size.
Gobel, Derek (Shell) | Briers, Jan (IPCOS N.V.) | de Boer, Frank (IPCOS BV) | Cramer, Ron (Shell Global Solutions) | Lai, Kok-Lam (Shell Global Solutions (Malaysia) Sdn.Bhd.) | Hooimeijer, Martijn (Shell India Markets Private Ltd)
This paper describes the successful application of Real-Time Optimization by Shell Malaysia E&P on the Integrated Gas Production System in Sarawak, implementing models for real-time monitoring and optimization of wells and facilities on a gas production network spanning more than 100 wells on more than 40 platforms across a number of different Production Sharing Contracts. We highlight how Digital Oil Field practices enable field-based data to be turned into information, support decision making, and lead to actions that ensure production is optimized continuously.
The technology described in this paper is applied to achieve consistent gas supply to meet demand, maximize revenue, and enable improved and timely operational decisions - striking a balance between short- and long-term value, and taking into account the reality of commercial and contractual constraints, finance, and economics. The optimization is data-driven and covers more than 1,000 variables and features multiple, mutually dependent objectives and constraints.
The solution has proven significantly better than prior physical model-based solutions, which deliver optimized field settings, but with inherently unstable results, and not fast enough for application in a real-time decision making environment. Field trials have proven a result of: increased condensate production at current or improved expected Ultimate Recovery, whilst maintaining a stable gas supply, fulfilling quality constraints and contractual LNG nominations.
This is one of the first successful attempts to implement truly-real-time optimization in a production environment of this size and complexity, including a complicated set of commercial and contractual constraints, and striking a transparent balance between short-term and long-term value. Having proven that a multi-departmental reality can be successfully captured and modeled, might mark the start of a transformation towards embedding intelligent energy to its true potential.
Recent advances in multiscale methods have shown great promise in modeling multiphase flow in highly detailed heterogeneous domains. Existing multiscale methods, however, solve for the flow field (pressure and total velocity) only. Once the fine-scale flow field is reconstructed, the saturation equations are solved on the fine scale. With the efficiency in dealing with the flow equations greatly improved by multiscale formulations, solving the saturation equations on the fine scale becomes the relatively more expensive part. In this paper, we describe an adaptive multiscale finite-volume (MSFV) formulation for nonlinear transport (saturation) equations. A general algebraic multiscale formulation consistent with the operator-based framework proposed by Zhou and Tchelepi (SPE Journal, June 2008, pages 267-273) is presented. Thus, the flow and transport equations are solved in a unified multiscale framework. Two types of multiscale operators--namely, restriction and prolongation--are used to construct the multiscale saturation solution. The restriction operator is defined as the sum of the fine-scale transport equations in a coarse gridblock. Three adaptive prolongation operators are defined according to the local saturation history at a particular coarse block. The three operators have different computational complexities, and they are used adaptively in the course of a simulation run. When properly used, they yield excellent computational efficiency while preserving accuracy. This adaptive multiscale formulation has been tested using several challenging problems with strong heterogeneity, large buoyancy effects, and changes in the well operating conditions (e.g., switching injectors and producers during simulation). The results demonstrate that adaptive multiscale transport calculations are in excellent agreement with fine-scale reference solutions, but at a much lower computational cost.